DIGITAL TECHNOLOGIES IN TEXTILE ART


by,

 Havva Halaceli

Cukurova University, Faculty of Fine Arts, Department of Textile Design,
Adana, Turkey

This is a digital age, dominated by information, communication and technology-based entertainment. This age is a result of rapid visual information-sharing. In this age, technology enables video sharing, saving every moment as visual data, and it is a result of rapid visual and information sharing. Today, artists use digital technologies as a means of expressing concepts. Woven textiles are also affected by the technological advances. Textiles have been essential for people from ancient times to now, for covering and protecting themselves from heat and cold. Weaving is a fine art form and a product of labor, including Coptic textiles and European tapestries; it can also utilize the speed, selection and color options of digital technologies that result from the mechanization and technological advances in the 20th century. Computerized Jacquard looms are one of the benefits of digital technologies that enable the weaving of complex imagery by allowing individual warp threads to be lifted.

Today, working with digital cameras, scanners and jacquard looms the textile artist becomes a designer and technology becomes a medium serving the artist’s creativity. In this study, the works of textile artists will be examined in view of time, technology and communication.
Keywords: Weaving, digital technology, jacquard loom

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Digital-Technologies-in-Textile-Art

Fiber Migration in yarn structure


Yarn structure plays a key role in determining the yarn physical properties and the performance characteristics of yarns and fabrics. The best way to study the internal structure of the yarns is to examine the arrangement of single fibers in the yarn body, and analyze their migration in crosswise and lengthwise fashions. This requires visual observation of the path of a single fiber in the yarn. Since a fiber is relatively a small element some specific techniques have to be utilized for its observation. In order to perform this task, two different experimental techniques have been developed by previous researchers.

a. Tracer fiber technique: This technique involves immersing a yarn, which contains a very small percentage of dyed fibers, in a liquid whose refractive index is the same as that of the original undyed fibers. This causes the undyed fibers to almost disappear from view and enables the observation of the path of a black dyed tracer fiber under a microscope. Dyed fibers are added to the raw stock before spinning to act as tracers. This technique was introduced by Morton and Yen .

b. Cross sectional method: In this method first the fibers in the yarn are locked in their original position by means of a suitable embedding medium, then the yarn is cut into thin sections, and these sections are studied under microscope. As in the tracer fiber technique, the yarn consists of mostly undyed fibers and a small proportion of dyed fibers such that there is no more than one dyed fiber in any yarn cross-section.

Fiber Migration

Fiber migration can be defined as the variation in fiber position within the yarn. Migration and twist are two necessary components to generate strength and cohesion in spun yarns. Twist increases the frictional forces between fibers and prevents fibers from slipping over one another by creating radial forces directed toward the yarn interior while fiber migration ensures that some parts of the all fibers were locked in the structure.

It was first recognized by Pierce that there is a need for the interchange of the fiber position inside a yarn since if a yarn consisted of a core fiber surrounded by coaxial cylindrical layers of other fibers, each forming a perfect helix of constant radius, discrete layers of the yarn could easily separate. Morton and Yen discovered that the fibers migrate among imaginary cylindrical zones in the yarn and named this phenomenon “fiber migration.”

Mechanisms Causing Fiber Migration

Morton [42] proposed that one of the mechanisms which cause fiber migration is the tension differences between fibers at different radial positions in a twisted yarn. During the twist insertion, fibers are subjected to different tensions depending on their radial positions. Fibers at the core will be under minimum tension due to shorter fiber path while fibers on the surface will be exposed to the maximum tension. According to the principle of the minimum energy of deformation, fibers lying near the yarn surface will try to migrate into inner zones where the energy is lower. This will lead to a cyclic interchange of fiber position. Later Hearle and Bose  gave another mechanism which causes migration. They suggested that when the ribbon-like fiber bundle is turned into the

clip_image002

Apart from the theoretical work cited above, several experimental investigations have been carried out during 1960’s to find out the possible factors affecting fiber migration. Results showed that the fiber migration can be influenced mainly by three groups of factors:

q fiber related factors such as fiber type, fiber length, fiber fineness, fiber initial modulus, fiber bending modulus and torsional rigidity;

q yarn related factors, such as yarn count and yarn twist ; and

q processing factors such as twisting tension, drafting system and number of doubling.

Methods for Assessing Fiber Migration

To study fiber migration Morton and Yen introduced the tracer fiber method. As explained in the previous section, this method enables the observation of the path of a single tracer fiber under a microscope. In order to draw the paths of the tracer fibers in the horizontal plane, Morton and Yen made measurements at successive peaks and troughs of the tracer images. Each peak and trough was in turn brought to register with the hairline of a micrometer eyepiece and scale readings were taken at a, b, and c as seen in Figure 22. The yarn diameter in scale units was given by c-a, while the offset of the

clip_image004

peak or trough, the fiber helix radii, was given by

The distance between

adjacent peaks and troughs was denoted by d. The overall extent of the tracer fiber was obtained from the images, as well. Morton and Yen concluded that in one complete cycle of migration, the fiber rarely crosses through all zones of the structure, from the surface of the yarn to the core and back again, which was considered as ideal migration.

clip_image006

Later Morton [42] used the tracer fiber method to characterize the migration quantitatively by means of a coefficient so called “the coefficient of migration.” He proposed that the intensity of migration i.e., completeness of the migration, or otherwise, of any migratory traverse could be evaluated by the change in helix radius between successive inflections of the helix envelope expressed as the fraction of yarn radius. For example intensity of migration in Figure 23 from A to B was stated as

clip_image008

where rA and rB are helix radius at A and B, respectively and R is yarn radius.

In order to express the intensity of migration for a whole fiber, Morton used the coefficient of migration, which is the ratio of actual migration amplitude to the ideal case. The coefficient of migration was given by

clip_image010

clip_image012

clip_image014

Merchant [ 1 ] modified the helix envelope by expressing the radial position in terms of (r / R) in order avoid any effects due to the irregularities in yarn diameter. The plot of (r / R) along the yarn axis gives a cylindrical envelope of varying radi
us around which the fiber follows a helical path. This plot is called a helix envelope profile. Expression of the radial position in terms of (r / R) involves the division of yarn cross sections into zones of equal radial spacing, which means fibers present longer lengths in the outer zones. Hearle et al. [18] suggested that it is more convenient to divide the yarn cross sections into zones of equal area so that the fibers are equally distributed between all zones. This was achieved by expressing the radial position in terms of (r / R)2, and the plot of (r / R)2against the length along the yarn is called a corrected helix envelope profile which presents a linear envelope for the ideal migration if the fiber packing density is uniform (Figure 24). The corrected helix envelope profile is much easier to manage analytically.

clip_image016

In 1964 Riding [52] worked on filament yarns, and expanded the tracer fiber technique by observing the fiber from two directions at right angles by placing a plane mirror near the yarn in the liquid with the plane of the mirror at 45° to the direction of observation. The radial position of the tracer fiber along the yarn was calculated by the following equation:

clip_image018

where x and y are the distances of the fiber from the yarn axis by the x and y co­ordinates; and dx and dy are the corresponding diameter measurements.

Riding also argued that it is unlikely that any single parameter, such as the coefficient of migration will completely characterize the migration behavior due to its statistical nature. He analyzed the migration patterns using the correlogram, or Auto-correlation Function and suggested that this analysis gives an overall statistical picture of the migration. Riding calculated the auto-correlation coefficient, rs from a series of

values of r / R for a separation of s intervals and obtained the correlogram for each experiment by plotting rs against s. Later a detailed theoretical study by Hearle and Goswami showed that the correlogram method should be used with caution because it tends to pick up only the regular migration.

Hearle and his co-researchers worked on a comprehensive theoretical and experimental analysis of fiber migration in the mid 1960’s. In Part I of the series Hearle, Gupta and Merchant came up with four parameters using an analogy with the method of describing an electric current to characterize the migration behaviors of fibers.

These parameters are:

i. the mean fiber position, which is the overall tendency of a fiber to be near the yarn surface or the yarn center.

clip_image020

ii. r.m.s deviation, which is the degree of the deviation from the mean fiber position

clip_image022

iii. mean migration intensity, which is the rate of change in radial position of a fiber.

clip_image024

iv. equivalent migration frequency, which is the value of migration frequency when an ideal migration cycle is formed from the calculated values of I and D.

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r is the current radial position of the fiber with respect to the yarn axis;

R is the yarn radius;

n is the number of the observations; and

Zn is the length of the yarn under consideration

By expressing the migration behavior in terms of these parameters, Hearle et al. replaced an actual migration behavior with a partial ideal migration which is linear with z (length along the fiber axis) but has the same mean fiber position, same r.m.s deviation, and the same mean migration intensity.

Later Hearle and Gupta [20] studied the fiber migration experimentally by using the tracer fiber technique. By taking into consideration the problem of asymmetry in the yarn cross section they came up with the following equation:

clip_image028

where

r1 and r2 are the helix radii

R1 and R2 are the yarn radii at position z1 and z2 along the yarn.

In 1972 Hearle et al. carried some experimental work on the migration in open-end spun yarns, and they observed that migration pattern in open-end yarns was considerably different from that of ring spun yarns. They suggested that this difference was the reason for the dissimilarity between mechanical and structural parameters of these two yarns.

Among numerous investigations of migration, there have been some attempts to develop a numerical algorithm to simulate yarn behavior. Possibly the most promising

and powerful approach was to apply a finite element analysis method to the mechanics of yarns.

One of the most recently published researches on the mathematical modeling of fiber migration in staple yarns was carried out by Grishanov, et al. They developed a new method to model the fiber migration using a Markov process, and claimed that all the main features of yarn structure could be modeled with this new method. In this approach the process of fiber migration was considered as a Poisson’s flow of events, and the fiber migration characteristics were expressed in terms of a transition matrix.

Another recent study was done by Primentas and Iype. They utilized the level of the focusing depth of a projection microscope as a measure of the fiber position along the z-axis with respect to the body of the yarn. Using a suitable reference depth they plotted the possible 3-dimensional configuration of the tracer fiber. In this study they assumed that yarn had a circular cross section and the difference between minimum and maximum values in depth represented the value of the vertical diameter, which was also equal to horizontal diameter. However, the yarn is irregular along its axis, and its cross section deviates from a circle. Besides, it is questionable that the difference between minimum and maximum values in depth would give the value of the vertical diameter. As these researchers stated this technique is in the “embryonic stage of development.”

Textile sector losing its sheen in Gujarat :Study


PTI | 06:11 PM,Nov 10,2011

Vadodara, Nov 10 (PTI) The textile manufacturing industry, the largest employer in Gujarat, after agriculture, is slowly losing its sheen in the state, according to a research study by Knight Frank India. The report provides extensive scenario of states of Gujarat, Maharashtra, Tamil Nadu, Andhra Pradesh and Karnataka that are identified as leaders in manufacturing capabilities. According to the study, released recently, the textile industy employs close to two lakh people accounting for 18 per cent of total manufacturing workforce in Gujarat. The share of textile in the state’s total manufacturing output has come down to 6 percent from 12 in the last ten years, it said. It further said that even the number of factories has decreased by 22 per cent to 1,523 from 1,957 a decade ago. This is in sharp contrast to Tamil Nadu where the number of factories in textile sector has increased by more than 1.5 times in the last 10 years. According to the Knight Frank Output Specialisation Matrix, textile is placed in the ‘Lost Opportunity’ quadrant meaning that the state is losing its specialisation in the sector compared to rest of India. Talking to PTI today, Dr Samantak Das, National Head, Research, Knight Frank India said, “The erstwhile textile hubs of Ahmedabad and Surat are gradually losing out to Tirupur and Coimbatore in Tamil Nadu. Going forward textile sector output will grow annually by 12 pc in next five years. However this will still be lower than Tamil Nadu’s 14 pc growth,” Das said. He however said the loss of textile in the state will be compensated by gains in emerging sectors such as Automobile and Engineering. “Gujarat has attracted huge amount of investment in the Automobile sector with companies such as Tata Motors, Peugeot and Ford setting up large production facilities in the state,” he said. (MORE) PTI COR DK DK

Ref:-http://ibnlive.in.com

Cotton Fibers 2


Once Valledupar's main economic produce; Cotton
Image via Wikipedia

WHAT IS COTTON?:

COTTON is defined as white fibrous substance covering seeds harvested from Cotton Plant.

SEED COTTON (called Kapas in India – Paruthi in Tamil)harvested from Cotton Plant.

LINT COTTON (RUIA in Hindi, PANJU in Tamil) is obtained by removing the seeds in a ginning machine.

LINT COTTON is spun into Yarn, which is woven or knitted into a Fabric. Researchers have found that cotton was grown more than 9000 years ago. However large scale cultivation commenced during middle of 17th Century AD.

Many varieties of Cotton are cultivated mainly from 3 important genetic species of Gossipium.

G. HIRSUTUM – 87% Grown in America, Africa, Asia, Australia Plant grows to a height of 2 Meters.

G. BARBADENSE– 8% Grown in America, Africa & Asia. Plant grows to a height of 2.5 Meters with yellow flowers, long fibers with good quality, fibers with long staple and fineness

G. Arboreum – 5% Perennial plant grows up to 2 meters with red flowers, poor quality fibers in East Africa and South East Asia.

There are four other species grown in very negligible quantities. Cotton harvested from the Plant by hand – picking or machine picking is ginned to remove seeds and the lint is pressed into Bales for delivery to Spinning Mills. Cotton is Roller Ginned (RG) or Saw Ginned (SG) depending varieties and ginning practices.

Cotton is cultivated in 75 Countries with an area of 32 Million Hectares. Cultivation period varies from 175 days to 225 days depending on variety. Cotton is harvested in two seasons, summer and winter seasons.

Saw ginned cotton is more uniform and cleaner than Roller Ginned Cotton. But fibers quality is retained better quality in Roller Ginning than Saw Ginning which has high productivity.

Cotton Fiber is having a tubular structure in twisted form. Now. researchers have developed coloured cotton also. As on date, percentage of Cotton fiber use is more than synthetic fibers. But, its share is gradually reducing. Cotton is preferred for under garments due its comfort to body skin. Synthetics have more versatile uses and advantage for Industrial purposes.

PROPERTIES OF COTTON

No other material is quite like cotton. It is the most important of all natural fibres, accounting for half of all the fibres used by the world’s textile industry.
Cotton has many qualities that make it the best choice for countless uses:
Cotton fibres have a natural twist that makes them so suitable for spinning into a very strong yarn.
The ability of water to penetrate right to the core of the fibre makes it easy to remove dirt from the cotton garments, and creases are easily removed by ironing.
Cotton fabric is soft and comfortable to wear close to skin because of its good moisture absorption qualities.
Charges of static electricity do not build up readily on the clothes.

HISTORY OF COTTON

Nobody seems to know exactly when people first began to use cotton, but there is evidence that it was cultivated in India and Pakistan and in Mexico and Peru 5000 years ago. In these two widely separated parts of the world, cotton must have grown wild. Then people learned to cultivate cotton plants in their fields.
In Europe, wool was the only fiber used to make clothing. Then from the Far East came tales of plants that grew “wool”. Traders claimed that cotton was the wool of tiny animals called Scythian lambs, that grew on the stalks of a plant. The stalks, each with a lamb as its flower, were said to bend over so the small sheep could graze on the grass around the plant. These fantastic stories were shown to be untrue when Arabs brought the cotton plant to Spain in Middle Ages.

In the fourteenth century cotton was grown in Mediterranean countries and shipped from there to mills in the Netherlands in western Europe for spinning and weaving. Until the mid eighteenth century, cotton was not manufactured in England, because the wool manufacturers there did not want it to compete with their own product. They had managed to pass a law in 1720 making the manufacture or sale of cotton cloth illegal. When the law was finally repealed in 1736, cotton mills grew in number. In the United States though, cotton mills could not be established, as the English would not allow any of the machinery to leave the country because they feared the colonies would compete with them. But a man named Samuel Slater, who had worked in a mill in England, was able to build an American cotton mill from memory in 1790.

GROWING THE COTTON

Cotton plant’s leaves resemble maple leaves and flowers look very much like pink mallow flowers that grow in swampy areas. They are relatives and belong in the same plant family.

Cotton is grown in about 80 countries, in a band that stretches around the world between latitudes 45 North to 30 South. For a good crop of cotton a long, sunny growing season with at least 160 frost-free days and ample water are required. Well drained, crumbly soils that can keep moisture well are the best. In most regions extra water must be supplied by irrigation. Because of it’s long growing season it is best to plant early but not before the sun has warmed the soil enough.

Seedlings appear about 5 days after planting the seeds. Weeds have to be removed because they compete with seedlings for water, light and minerals and also encourage pests and diseases. The first flower buds appear after 5-6 weeks, and in another 3-5 weeks these buds become flowers.
Each flower falls after only 3 days leaving behind a small seed pot, known as the boll. Children in cotton-growing areas in the South sometimes sing this song about the flowers:
First day white, next day red,
third day from my birth – I’m dead.
Each boll contains about 30 seeds, and up to 500 000 fibres of cotton. Each fibre grows its full length in 3 weeks and for the following 4-7 weeks each fiber gets thicker as layers of cellulose build up the cell walls. While this is happening the boll matures and in about 10 weeks after flowering it splits open. The raw cotton fibres burst out to dry in the sun. As they lose water and die, each fibre collapses into what looks like a twisted ribbon. Now is time for harvesting. Most cotton is hand-picked. This is the best method of obtaining fully grown cotton because unwanted material, called “trash”, like leaves and the remains of the boll are left behind. Also the cotton that is too young to harvest is left for a second and third picking. A crop can be picked over a period of two months as the bolls ripen. Countries that are wealthy and where the land is flat enough usually pick cotton with machines – cotton harvesters.

GLOBAL COTTON – VATIETIES – PLANTING AND HARVESTING PERIODS

SNo Country Planting Period Harvesting Staple-mm Mike Variety
1 AFGHANISTAN APRIL-MAY OCT-DEC 26-28 4.0 ACALA
2 ARGENTINA SEPT-OCT FEB-JUNE 24-28 3.9-4.1 TOBA
3 AUSTRALIA SEPT-NOV MAR-JUNE 24-29 3.2-4.9 DPL
4 BRAZIL OCT-NOV MAR-JUNE 26-28 3.2-4.0 IAC
BRAZIL PERENNIAL 32-35 3.2-4.8 MOCO
5 BURKIN JUNE-JULY NOV-DEC 25-28 3.6-4.8 ALLEN
6 CAMERRON JUNE NOV-DEC 25-28 3.8-4.3 ALLEN
7 CENTRAL AFRICA JUN-JULY NOV-DEC 25-28 3.8-4.2 ALLEN
8 CHAD JUNE NOV-DEC 25-28 3.8-4.4 ALLEN
9 CHINA APRIL-JUNE SEP-OCT 22-28 3.5-4.7 SHANDONG
XINJIANG
MNH-93
10 COTED IVORIE JUN-AUG OCT-JAN 24-28 2.6-4.6 ALLEN
11 EGYPT MARCH SEP-OCT 31-40 3.24.6 GIZA
12 GREECE APRIL SEPT-OCT 26-28 3.8-4.2 4S
13 INDIA APRIL-NOV SEP-NOV 16-38 2.8-7.9 SEPARATE LIST
INDIA SEPT-NOV FEB-APR
14 IRAN MAR-APR SEP-NOV 26-28 3.9-4.5 COKER
15 ISRAEL APRIL SEP-OCT 26-37 3.5-4.3 ACALA
PIMA
16 KAZAKSTAN APR-MAY SEP-NOV
17 MALI JUN-JUL OCT-NOV 26-27 3.7-4.5 BJA
18 MEXICO MAR-JUNE AUG-DEC 26-29 3.5-4.5 DELTAPINE
19 MOZAMBIQUE NOV-DEC APR-MAY 25-29 3.6-4.2 A637
20 NIGARIA JUL-AUG DEC-FEB 24-26 2.5-4.0 SAMARU
21 PAKISTAN APR-JUN SEP-DEC 12-33 3.5-6.0
22 PARAGUAY OCT-DEC MAR-APR 26-28 3.3-4.2 EMPIRE
23 PERU JUL-NOV FEB-AUG 29-.8 3.3-4.2 TANGUIS
PIMA
24 SPAIN APR-MAY SEP-NOV 25-28 3.3-4.9 CAROLINA
25 SUDAN AUG JUN-APR 27-E0 3.8-4.2 BARAKAT
ACALA
26 SYRIA APR-MAY SEP-NOV 25-29 3.8-4.8 ALEPPO
27 TAZIKSTAN APR-MAY SEP-NOV
28 TOGO JUN-JUL NOV-DEC 28-29 4.3-5.5 ALLEN
29 TURKMENISTAN APR-MAY SEP-NOV 24-29 3.5-5.5 DELTAPINE
COKER
30 TURKEY APR-MAY SEP-NOV 24-28 3.5-5.5 DELTAPINE
31 UGANDA APR-JUN NOV-FEB 26-28 3.3-4.8 BAP-SATU
32 UZBEKISTAN APR-MAY SEP-NOV 24-41 3.5-4.7
33 USA APR-MAY SEP-DEC 26-40 3.8-4.5 VARIETIES
28-30 3.0-4.0 ACALA 151T
28-29 3.8-4.6 DELTAPINENC
25-28 3.2-4.6 PAYMASTER 280
27-28 3.7-4.7 STONOVILLE ST
35-40 3.5-4.5 PIMA S7
34 YEMEN AUG-SEP JUN-APR 36-40 3.5-4.9 K4

COTTON AND YARN QUALITY CO-RELATION:

Instead of buying any cotton available at lowest price, spinning it to produce yarn of highest count possible and selling Yam at any market in random, it is advisable to locate a good market where Yarn can be sold at highest price and select a Cotton which has characteristics to spin Yarn of desired specifications for that market.

ESSENTIAL CHARACTERISTICS of cotton quality and characteristics of Yarn quality of Yarn are given from detailed experimental investigations. Some of the important conclusions which help to find co-relation between Yarn quality and Cotton quality are given below

  • STAPLE LENGTH: If the length of fiber is longer, it can be spun into finer counts of Yarn which can fetch higher prices. It also gives stronger Yarn.
  • STRENGTH : Stronger fibers give stronger Yarns. Further, processing speeds can be higher so that higher productivity can be achieved with less end-breakages.
  • FIBER FINENESS: Finer Fibers produce finer count of Yarn and it also helps to produce stronger Yarns.
  • FIBER MATURITY : Mature fibers give better evenness of Yarn. There will be less end – breakages . Better dyes’ absorbency is additional benefit.
  • UNIFORMITY RATIO: If the ratio is higher. Yam is more even and there is reduced end-breakages.
  • ELONGATION :A better value of elongation will help to reduce end-breakages in spinning and hence higher productivity with low wastage of raw material.
  • NON-LINT CONTENT: Low percentage of Trash will reduce the process waste in Blow Room and cards. There will be less chances of Yarn defects.
  • SUGAR CONTENT: Higher Sugar Content will .create stickiness of fiber and create processing problem of licking in the machines.
  • MOISTURE CONTENT : If Moisture Content is more than standard value of 8.5%, there will be more invisable loss. If moisture is less than 8.5%, then there will be tendency for brittleness of fiber resulting in frequent Yarn breakages.
  • FEEL : If the feel of the Cotton is smooth, it will be produce more smooth yarn which has potential for weaving better fabric.
  • CLASS : Cotton having better grade in classing will produce less process waste and Yarn will have better appearance.
  • GREY VALUE: Rd. of calorimeter is higher it means it can reflect light better and Yam will give better appearance.
  • YELLOWNESS : When value of yellowness is more, the grade becomes lower and lower grades produce weaker & inferior yarns.
  • NEPPINESS : Neppiness may be due to entanglement of fibers in ginning process or immature fibers. Entangled fibers can be sorted out by careful processing But, Neps due to immature fiber will stay on in the end product and cause the level of Yarndefects to go higher.

An analysis can be made of Yarn properties which can be directly attributed to cotton quality.

1. YARN COUNT: Higher Count of Yarn .can be produced by longer, finer and stronger fibers.

2. C.V. of COUNT: Higher Fiber Uniformity and lower level of short fiber percentage will be beneficial to keep C.V.(Co-efficient of Variation) at lowest.

3. TENSILE STRENGTH : This is directly related to fiber strength. Longer Length of fiber will also help to produce stronger yarns.

4. C.V. OF STRENGTH : is directly related CV of fiber strength.

5. ELONGATION : Yam elongation will be beneficial for weaving efficiently. Fiber with better elongation have positive co-relation with Yarn elongation.

6. C.V. OF ELONGATION: C.V. of Yarn Elongation can be low when C.V. of fiber elongation is also low.

7. MARS VARIATION : This property directly related to fiber maturity and fiber uniformity.

8. HAIRINESS : is due to faster processing speeds and high level of very short fibers,

9. DYEING QUALITY : will defend on Evenness of Yarn and marketing of cotton fibers.

10. BRIGHTNESS : Yarn will give brighter appearance if cotton grade is higher.

COTTON QUALITY SPECIFICATIONS:

The most important fiber quality is Fiber Length

Length

Staple
classification
Length mm Length inches Spinning Count
Short Less than 24 15/16 -1 Coarse Below 20
Medium 24- 28 1.1/132-1.3/32 Medium Count 20s-34s
Long 28 -34 1.3/32 -1.3/8 Fine Count 34s – 60s
Extra Long 34- 40 1.3/8 -1.9/16 Superfine Count 80s – 140s

Notes:

  • Spinning Count does not depend on staple length only. It also depends on fineness and processing machinery.
  • Length is measured by hand stapling or Fibrograph for 2.5% Span Length
  • 2.5%SL (Spun Length) means at least 2.5% of total fibers have length exceeding this value.
  • 50% SL means at least 50% of total fibers have length exceeding this value.

LENGTH UNIFORMITY

Length Uniformity is Calculated by 50SL x 100 / 2.5 SL

Significance of UR (Uniformity Radio) is given below:

UR% Classification 50-55
Very Good 45-50 Good 40-45
Satisfactory 35-40
Poor Below 30 Unusable
M= 50% SL
UHM SL – Average value of length of Longest of 50% of Fibers
UI Uniformity Index
UI M/UHM

Interpretation of Uniformity Index

U.INDEX CLASSIFICATION UHM CLASSIFICATION
Below 77 Very low Below 0.99 Short
77-99 Low 0.99-1.10 Medium
80-82 Average 1.11-1.26 Long
83-85 High Above 1.26 Extra Long
Above 85 Very High

Now Uniformity is measured by HVI

Fiber Strength

Fiber Strength, next important quality is tested using Pressley instrument and the value is given in Thousands of Pounds per Square inch. (1000 psi) For better accuracy, Stelometer is used and results are given in grams / Tex.

Lately, strength is measured in HVI (High Value Instrument) and result is given in terms of grams/tex.

Interpretation of Strength value is given below

G/tex Classification
Below 23 Weak
24-25 Medium
26-28 Average
29-30 Strong
Above 31 Very Strong

Strength is essential for stronger yarns and higher processing speeds.

  • Fiber Fineness Fiber Fineness and maturity are tested in a conjunction using Micronaire Instrument.
  • Finer Fibers give stronger yarns but amenable for more neppiness of Yarn due to lower maturity.
  • Micronaire values vary from 2.6 to 7.5 in various varieties.

FINENESS AND MATURITY

Usually Micronaire value is referred to evaluate fineness of Cotton and its suitability for spinning particular count of Yarn. As the value is a combined result of fineness and maturity of Cotton fiber, it cannot be interpreted, property for ascertaining its spinning Value. This value should be taken in conjunction with standard value of Calibrated Cotton value.

The following table will explain that micronaire value goes up along with maturity but declines with thickness of fiber. An Egyptian variety of Cotton, three samples of High maturity. Low maturity and Medium maturity were taken and tested. Test results are given below,

Maturity Micronaire Perimeter Maturity Maturity Ratio
High 4.3 52.9 85.1 1.02
Medium 4.0 54.4 80.1 0.96
Low 3.9 54.7 79.3 0.95

Here, Micronaire Value of 4.3 is higher than 3.9 of low maturity cotton Another Greek Cotton was tested and results are give below

High 3.8 57.0 75.1 0.88
Medium 3.5 54.9 70.7 0.84
Low 3.2 55.2 65.8 0.80

Micronaire Value of 3.8 is higher than 3.2 of low maturity cotton. Another American Cotton was tested and results are as follows

High 4.1 64.4 75.9 0.87
Medium 3.4 62.1 68.0 0.80
Low 2.7 59.8 56.1 0.67

Hence, it is essential to know what Micronaire value is good for each variety of Cotton.

Maturity Ratio Classification
1.00 and above Very Mature
0.95 – 1.0 Above Average
0.85 – 0.95 Mature
0.80 – 0.85 Below Average
Less than 0.80 immature

COTTON GRADE

Cotton grade is determined by evaluating colour, leaf and ginning preparation. Higher grade cottons provide better yarn appearance and reduced process waste.

Colour is determined by using Nickerson-Hunter Calorimeter. This gives values Rd (Light or Dark) and +b (Yellowness).

AMERICAN UPLAND COTTONS ARE CLASSIFIED
ACCORDING TO GRADES AS GIVEN BELOW

WHITE COLOUR

S.NO GRADE SYMBOL CODE
1 GOOD MIDDLING GM 11
2 STRICT MIDDLING SM 21
3 MIDDLING M 31
4 STRICT LOW MIDDLING SLM 41
5 LOW MIDDLING LM 51
6 STRICT GOOD ORDINARY SGO 61
7 GOOD ORDINARY GO 71
8 BELOW GRADE

Similar grading is done for Light Spotted, Spotted, Tinged and Yellow Stained Cottons. PIMA cottons are graded I to 9

HOW TO BUY COTTON?

COTTON BUYING is the most important function that will contribute to optimum profit of a Spinning Mill.

EVALUATION of cotton quality is generally based more on experience rather than scientific testing of characteristics only.

TIMING of purchase depends on comprehensive knowledge about various factors which affect the prices.

CHOOSING the supplier for reliability of delivery schedules and ability to supply cotton within the prescribed range of various parameters which define the quality of Cotton.

BARGINING for lowest price depends on the buyer’s reputation for prompt payment and accept delivery without dispute irrespective of price fluctuations.

ORGANISING the logistics for transportation of goods and payment for value of goods will improve the benefits arising out of the transaction.

PROFIT depends on producting high quality Yarn to fetch high prices. Influence of quality of raw material is very important in producing quality Yarn. But, quality of yam is a compound effect of quality of raw material, skills of work-force, performance of machines,- process know-how of Technicians and management expertise.

A good spinner is one who produces reasonably priced yarn of acceptable quality from reasonably priced fiber. Buying a high quality, high priced cotton does not necessarily result in high quality Yarn or high profits.

GUIDELINES FOR COTTON CONTRACTS:

Buyer and seller should clearly reach correct understanding on the following factors.

1. Country of Origin, Area of Growth, Variety, Crop year

2. Quality – Based on sample or

Description of grade as per ASTM standard or sample
For grade only and specifying range of staple length,
Range of Micronaire, range of Pressley value, uniformity,
Percentage of short fiber, percentage of non-lint content,
Tolerable level of stickiness

3. Percentage of Sampling at destination

4. Procedure for settling disputes on quality or fulfillment of contract obligations.

5. Responsibility regarding contamination or stickiness.

6. Price in terms of currency, Weight and place of delivery.

7. Shipment periods

8. Certified shipment weights or landing Weights

9. Tolerances for Weights and Specifications

10. Port of Shipment and port of destination, partial shipments allowed or not, transshipment allowed or not, shipments in containers or Break-bulk carriers

11. Specifications regarding age of vessels used for shipment, freight payment in advance or on delivery

12. Responsibility regarding Import & Export duties

13. Terms of Insurance cover

14. Accurate details of Seller, Buyer and Broker

15. Terms of Letter of. Credit regarding bank .negotiation, reimbursement and special conditions, if any

Choose Correct Supplier or Agent:

Apart from ensuring correct terms of Contract, Buyer should ensure that purchase is made from Reliable Supplier or through a Reliable Agent. Some suppliers evade supplies under some pretext if the market goes up. Otherwise, they supply inferior quality Either way buyer suffers.

By establishing long term relationship will reliable Suppliers, Buyers can have satisfaction of getting correct quality, timely deliveries and fair prices.

CHOOSING SUPPLIER:

It is good to establish long term relationship with a few Agents who represent reputed Trading Companies in various Cotton Exporting Countries. They usually give reliable market information on quality, prices and market trends so that buyer can take intelligent decision. As cotton is not a manufactured Commodity, it is good to buy from dependable suppliers, who will ensure supply of correct quality with a variation within acceptable limits at correct price and also deliver on due date.

CHOOSING QUALITY:

In a market with varying market demand situation. Buyers should decide which counts of Yarn to spin. Buyer can call for samples suitable for spinning Yarn counts programmed for production. Many spinners plan to do under-spinning. For Example, cotton suitable for 44s is used for spinning 40s. Some spinners do over-spinning. They buy cotton suitable for 40s and spin 44s count. But, is advisable to spin optimum count to ensure quality and also keep cost of raw material at minimum level as for as possible. Some spinners also buy 2 or more varieties and blend them for optimum spinning. For’ this purpose, a good knowledge to evaluate cotton quality and co-relate with yarn properties of required specifications. Cotton buyer should develop expertise in assessing cotton quality. Machine tests must be done only to confirm manual evaluation.

TAKING RIGHT OPTION:

It is not advisable just to look at price quoted by supplier. Correct costing should be done to work out actual cost when the cotton arrives at Mills. Further lowest price does not always mean highest profit for buying. Profitability may be affected by anyone or more of the following factors.

  • If the trash is higher, more waste will be produced reducing the Yarn out- turn and hence profit.
  • If the uniformity is less, end – breakages will be more reducing productivity and profitability.
  • If grade is poor or more immature fibers are found in cotton, the yarn appearance will be affected and Yarn will fetch lesser price in the market.
  • If the transit period for transport of cotton is longer, then also profitability will be reduced due blocking of funds for a longer period and increased cost of Interest.
  • Rate of Sales Tax varies from State to State. This must be taken in to account.
  • Hence, thorough costing should be worked out before deciding on the quoted pnce onlv

The margin of profit in spinning cotton should be calculated before deciding on The various options available depending on market conditions should be studied.

The factors to be considered for taking options are as follows.

  • Count for which demand is good in market
  • Prices for various counts for which demand exists.
  • Cost of manufacturing various counts.
  • Adequacy of machinery for the selected count.
  • Various varieties of cotton available for spinning the selected count.
  • Profit margin for each count using different varieties.
  • Price quoted by different Agents for same variety of selected cotton.
  • Reliability of supplier for quality and timely delivery.

Cost Consideration:

Apart from the price quoted by the seller, other incidental costs must be taken into consideration before buying.

a) Duration for goods to reach Buyer’s godown from the seller’s Warehouse. If the duration is longer, buyer will incur higher interest charges.

b) Cost of Transportation and taxes.

Resolution of differences

If any discrepancy arises in the quality, weight and delivery periods, sellers should be willing to resolve the differences amicably and quickly. In case the matter is referred to Arbitrator, the award of the Arbitrator must be immediately enforced.

Bench Marks for Easy Reference

It is better if quality bench marks are established for different varieties so that buying decisions are easy for buyers Following standards have been found to be appropriate for Strict Middling Grade Cotton of staple 1.3/32″.

  1. Staple Length ( 2.5% Spun Length) – Minimum 1.08″ or 27.4 mm
  2. Micronaire : Minimum 3.8, Maximum-4.6 Variation within bulk sample should not be more than _ 0.1
  3. Colour : Rd not less than 75 not more than 10
  4. Nep Content: Less than 150 per gram
  5. Strength : More than 30 grams/tex
  6. Length Uniformity Ratio: Not less than 85%
  7. Elongation : More than 8%
  8. Short Fiber Content: Less than 5%
  9. Seed Count Fragments : Less than 15 per grams
    1. Commercial Bench marks can be given as follows:
      1. Price Competitiveness
      2. Price Stability
      3. Easy Availability throughout year
      4. Uniform Classing and Grading system
      5. Even- running Cotton in all Characteristics
      6. Reliable deliveries òr Respect for sanctity of contract.

QUALITY EVALUATION:

The need for quality evaluation is for following purposes

a) To get optimum quality at lowest price.
b) To decide whether cotton bought will can be processed to spin Yarn of desired specifications.
c) To check the quality of sample cotton with quality of delivered cotton.
d) To decide about correct machine settings and speeds for processing the cotton
e) To estimate profitability of purchase decisions.

Knowing the cotton properties is only half the battle for profits. It needs expertise to know how to get best of its value.

Currently popular instrument called HVI gives ready information on various parameters to make correct purchase decisions.

If may not be possible to get all the desired qualities in one variety or one lot of Cotton. In such case, an intelligent decision to select best combination of different varieties or lots to get desired Yam quality is necessary to get optimum yarn quality at optimum cost.

If correct evaluation is made, profits are large. Hence, evaluation of quality is essential for optimum profit making and also make the customers happy with supply of correct quality of Yarn.

Expert classers can manage to achieve reasonable level of correct evaluation. Now, with availability of better instruments, it is better to check qualities to make sure that desired quality of cotton is procured.  These details should give cotton buyer reasonable guidance to make correct evaluation of cotton quality and ensure its suitability for producing required quality of yarn.

QUALITY EVALUATION        CHARACTERISTICS CO-RELATION TO YARN
1. Staple Length Spinning Potential
2. Fiber Strength Yarn strength, less Breakages
3. Fineness   Finer Spinning Potential
4. Maturity Yarn Strength and even ness, better dyeing
5. Non-Lint.content (Trash) Reduced Waste
6, Uniformity Ratio Better productivity and Evenness
7. Elongation Less end Breakages
8, Friction Cohesiveness
9. Class Yarn Appearance
10.Stickiness Spinning problem by lapping & Dyeing quality
11. Grey Value Yarn lustre
12. Yellowness Yarn Appearance
13.Neppiness Yarn neppiness
14. Moisture Content 8.5% moisture content optimum for spinning at 65%

QUALITY TESTING INSTRUMENTS:

Instrument Measurements
Fibrogaph   Length
Pressley Apparatres Fiber Bundle Strength
HV I Instrument Length, Strength, Uniformity, Elongation, Micronaire, Color and Trash
Stelometer Instrument Strength, Elongation
Micronaire Combined test of fineness & maturity
Shirley Trash Analyser Trash Content
Manual Test Class & staple length
Moisture Meter Moisture
Colorimeter Grey value & yellow ness. Brightness
Polarised light Microscope or
Casricaire test
Maturity
Photographic film   Neppiness

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Digg This

Cotton Fiber 1


I took photo with Canon camera.
Image via Wikipedia

COTTON FIBRE GROWTH:

  • Improvements in cotton fiber properties for textiles depend on changes in the growth and development of the fiber.
  • Manipulation of fiber perimeter has a potential to impact the length, micronaire, and strength of cotton fibers. The perimeter of the fiber is regulated by biological mechanisms that control the expansion characteristic of the cell wall and establish cell diameter.
  • mprovements in fiber quality can take many different forms. Changes in length, strength, uniformity, and fineness   In one recent analysis, fiber perimeter was shown to be the single quantitative trait of the fiber that affects all other traits . Fiber perimeter is the variable that has the greatest affect on fiber elongation and strength properties. While mature dead fibers have an elliptical morphology, living fibers have a cylindrical morphology during growth and development. Geometrically, perimeter is directly determined by diameter (perimeter = diameter × p). Thus, fiber diameter is the only variable that directly affects perimeter. For this reason, understanding the biological mechanisms that regulate fiber diameter is important for the long-term improvement of cotton.
  • A review of the literature indicates that many researchers believe diameter is established at fiber initiation and is maintained throughout the duration of fiber development . A few studies have examined, either directly or indirectly, changes in fiber diameter during development. Some studies indicate that diameter remains constant ; while others indicate that fiber diameter increases as the fiber develops.
  • The first three stages occur while the fiber is alive and actively growing. Fiber initiation involves the initial isodiametric expansion of the epidermal cell above the surface of the ovule. This stage may last only a day or so for each fiber. Because there are several waves of fiber initiation across the surface of the ovule , one may find fiber initials at any time during the first 5 or 6 d post anthesis. The elongation phase encompasses the major expansion growth phase of the fiber. Depending on genotype, this stage may last for several weeks post anthesis. During this stage of development the fiber deposits a thin, expandable primary cell wall composed of a variety of carbohydrate polymers . As the fiber approaches the end of elongation, the major phase of secondary wall synthesis starts. In cotton fiber, the secondary cell wall is composed almost exclusively of cellulose. During this stage, which lasts until the boll opens (50 to 60 d post anthesis), the cell wall becomes progressively thicker and the living protoplast decreases in volume. There is a significant overlap in the timing of the elongation and secondary wall synthesis stages. Thus, fibers are simultaneously elongating and depositing secondary cell wall.
  • The establishment of fiber diameter is a complex process that is governed, to a certain extent, by the overall mechanism by which fibers expand. The expansion of fiber cells is governed by the same related mechanisms occurring in other walled plant cells. Most cells exhibit diffuse cell growth, in which new wall and membrane materials are added throughout the surface area of the cell. Specialized, highly elongated cells, such as root hairs and pollen tubes, expand via tip synthesis where new wall and membrane materials are added only at a specific location that becomes the growing tip of the cell. While the growth mechanisms for cotton fiber have not been fully documented, recent evidence indicates that throughout the initiation and early elongation phases of development, cotton fiber expands primarily via diffuse growth . Later in fiber development, late in cell elongation, and well into secondary cell wall synthesis (35 d post anthesis), the organization of cellular organelles is consistent with continued diffuse growth . Many cells that expand via diffuse growth exhibit increases in both cell length and diameter; but cells that exhibit tip synthesis do not exhibit increases in cell diameter . If cotton fiber expands by diffuse growth, then it is reasonable to suggest that cell diameter might increase during the cell elongation phase of development.
  • Cell expansion is also regulated by the extensibility of the cell wall. For this reason, cell expansion most commonly occurs in cells that have only a primary cell wall . Primary cell walls contain low levels of cellulose. Production of the more rigid secondary cell wall usually signals the cessation of cell expansion. Secondary cell wall formation is often indicated by the development of wall birefringence.
  • Analyses of fiber diameter and cell wall birefringence show that fiber diameter significantly increased as fibers grew and developed secondary cell walls. Both cotton species and all the genotypes tested exhibited similar increases in diameter; however, the specific rates of change differed. Fibers continued to increase in diameter during the secondary wall synthesis stage of development, indicating that the synthesis of secondary cell wall does not coincide with the cessation of cell expansion.

GINNING

  • The generally recommended machinery sequence at gins for spindle-picked cotton is rock and green-boll trap, feed control, tower drier, cylinder cleaner, stick machine, tower drier, cylinder cleaner, extractor feeder, gin stand, lint cleaner, lint cleaner, and press.
  • Cylinder cleaners use rotating spiked drums that open and clean the seedcotton by scrubbing it across a grid-rod or wire mesh screen that allows the trash to sift through. The stick machine utilizes the sling-off action of channel-type saw cylinders to extract foreign matter from the seedcotton by centrifugal force. In addition to feeding seedcotton to the gin stand, the extractor feeder cleans the cotton using the stick machine’s sling-off principle.
  • In some cases the extractor-feeder is a combination of a cylinder cleaner and an extractor.    Sometimes an impact or revolving screen cleaner is used in addition to the second cylinder cleaner. In the impact cleaner, seedcotton is conveyed across a series of revolving, serrated disks instead of the grid-rod or wire mesh screen.
  • Lint cleaners at gins are mostly of the controlled-batt, saw type. In this cleaner a saw cylinder combs the fibers and extracts trash from the lint cotton by a combination of centrifugal force, scrubbing action between saw cylinder and grid bars, and gravity assisted by an air current
  • Seedcotton-type cleaners extract the large trash components from cotton. However, they have only a small influence on the cotton’s grade index, visible liint foreign-matter content, and fiber length distribution when compared with the lint cleaning effects.  Also, the number of neps created by the entire seedcotton cleaning process is about the same as the increase caused by one saw-cylinder lint cleaner.
  • Most cotton gins today use one or two stages of saw-type lint cleaners. The use of too many stages of lint cleaning can reduce the market value of the bale, because the weight loss may offset any gain from grade improvement. Increasing the number of saw lint cleaners at gins, in addition to increasing the nep count and short-fiber content of the raw lint, causes problems at the spinning mill. These show up as more neps in the card web and reduced yarn strength and appearance .
  • Pima cotton, extra-long-staple cotton, is roller ginned to preserve its length and to minimize neps. To maintain the highest possible quality bale of pima cotton, mill-type lint cleaners were for a long time the predominant cleaner used by the roller-ginning industry. Today, various combinations of impacts, incline, and pneumatic cleaners are used in most roller-ginning plants to increase lint-cleaning capacity.

COTTON FIBER QUALITY:

  •     Two simple words, fiber quality, mean quite different things to cotton growers and to cotton processors.    No after-harvest mechanisms are available to either growers or processors that can improve intrinsic fiber quality.
    Most cotton production research by physiologists and agronomists has been directed toward improving yields, so the few cultural-input strategies suggested for improving fiber quality during the production season are of limited validity. Thus, producers have limited alternatives in production practices that might result in fibers of acceptable quality and yield without increased production costs.
    Fiber processors seek to acquire the highest quality cotton at the lowest price, and attempt to meet processing requirements by blending bales with different average fiber properties. Of course, bale averages for fiber properties do not describe the fiber-quality ranges that can occur within the bales or the resulting blends. Further, the natural variability among cotton fibers unpredictably reduces the processing success for blends made up of low-priced, lower-quality fibers and high-priced, higher-quality fibers.
    Blends that fail to meet processing specifications show marked increases in processing disruptions and product defects that cut into the profits of the yarn and textile manufacturers. Mill owners do not have sufficient knowledge of the role classing-office fiber properties play in determining the outcome of cotton spinning and dyeing processes.
    Even when a processor is able to make the connection between yarn and fabric defects and increased proportions of low-quality fibers, producers have no way of explaining why the rejected bales failed to meet processing specifications when the bale averages for important fiber properties fell within the acceptable ranges.
    If, on the other hand, the causes of a processing defect are unknown, neither the producer nor the processor will be able to prevent or avoid that defect in the future. Any future research that is designed to predict, prevent, or avoid low-quality cotton fibers that cause processing defects in yarn and fabric must address the interface between cotton production and cotton processing.
    Every bale of cotton produced in the USA crosses that interface via the USDA-AMS classing offices, which report bale averages of quantified fiber properties. Indeed, fiber-quality data reports from classing offices are designed as a common quantitative language that can be interpreted and understood by both producers and processors. But the meaning and utility of classing-office reports can vary, depending on the instrument used to evaluate.
  • Fiber maturity is a composite of factors, including inherent genetic fineness compared with the perimeter or cross section achieved under prevailing growing conditions and the relative fiber cell-wall thickness and the primary -to- secondary fiber cell-wall ratio, and the time elapsed between flowering and boll opening or harvest. While all the above traits are important to varying degrees in determining processing success, none of them appear in classing-office reports.
  • Micronaire, which is often treated as the fiber maturity measurement in classing-office data, provides an empirical composite of fiber cross section and relative wall thickening. But laydown blends that are based solely on bale-average micronaire will vary greatly in processing properties and outcomes.
    Cotton physiologists who follow fiber development can discuss fiber chronological maturity in terms of days after floral anthesis. But, they must quantify the corresponding fiber physical maturity as micronaire readings for samples pooled across several plants, because valid micronaire determinations require at least 10 g of individualized fiber.
  • Some fiber properties, like length and single fiber strength, appear to be simple and easily understood terms. But the bale average length reported by the classing office does not describe the range or variability of fiber lengths that must be handled by the spinning equipment processing each individual fiber from the highly variable fiber population found in that bale.
    Even when a processing problem can be linked directly to a substandard fiber property, surprisingly little is known about the causes of variability in fiber shape and maturity. For example:
  • Spinners can see the results of excessive variability in fiber length or strength when manifested as yarn breaks and production halts.Knitters and weavers can see the knots and slubs or holes that reduce the value of fabrics made from defective yarns that were spun from poor-quality fibre
  • Inspectors of dyed fabrics can see the unacceptable color streaks and specks associated with variations in fiber maturity and the relative dye-uptake success.
  • The grower, ginner, and buyer can see variations in color or trash content of ginned and baled cotton.

But there are no inspectors or instruments that can see or predict any of the above quality traits of fibers while they are developing in the boll.    There is no definitive reference source, model, or database to which a producer can turn for information on how cultural inputs could be adapted to the prevailing growth conditions of soil fertility, water availability, and weather (temperature, for example) to produce higher quality fiber.

The scattered research publications that address fiber quality, usually in conjunction with yield improvement, are confusing because their measurement protocols are not standardized and results are not reported in terms that are meaningful to either producers or processors. Thus, physiological and agronomic studies of fiber quality frequently widen, rather than bridge, the communication gap between cotton producers and processors.

This overview assembles and assesses current literature citations regarding the quantitation of fiber quality and the manner in which irrigation, soil fertility, weather, and cotton genetic potential interact to modulate fiber quality. The ultimate goal is to provide access to the best answers currently available to the question of what causes the annual and regional fiber quality variations

From the physiologist’s perspective, the fiber quality of a specific cotton genotype is a composite of fiber shape and maturity properties that depend on complex interactions among the genetics and physiology of the plants producing the fibers and the growth environment prevailing during the cotton production season.

Fiber shape properties, particularly length and diameter, are largely dependent on genetics. Fiber maturity properties, which are dependent on deposition of photosynthate in the fiber cell wall, are more sensitive to changes in the growth environment. The effects of the growth environment on the genetic potential of a genotype modulate both shape and maturity properties to varying degrees.

Anatomically, a cotton fiber is a seed hair, a single hyperelongated cell arising from the protodermal cells of the outer integument layer of the seed coat. Like all living plant cells, developing cotton fibers respond individually to fluctuations in the macro- and microenvironments. Thus, the fibers on a single seed constitute continua of fiber length, shape, cell-wall thickness, and physical maturity .

Environmental variations within the plant canopy, among the individual plants, and within and among fields ensure that the fiber population in each boll, indeed on each seed, encompasses a broad range of fiber properties and that every bale of cotton contains a highly variable population of fibers.

Successful processing of cotton lint depends on appropriate management during and after harvest of those highly variable fiber properties that have been shown to affect finished-product quality and manufacturing efficiency . If fiber-blending strategies and subsequent spinning and dyeing processes are to be optimized for specific end-uses and profitability, production managers in textile mills need accurate and effective descriptive and predictive quantitative measures of both the means and the ranges of these highly variable fiber properties .

In the USA, the components of cotton fiber quality are usually defined as those properties reported for every bale by the classing offices of the USDA-AMS, which currently include length, length uniformity index, strength, micronaire, color as reflectance (Rd) and yellowness (+b), and trash content, all quantified by the High Volume Instrument (HVI) line. The classing offices also provide each bale with the more qualitative classers’ color and leaf grades and with estimates of preparation (degree of roughness of ginned lint) and content of extraneous matter.

The naturally wide variations in fiber quality, in combination with differences in end-use requirements, result in significant variability in the value of the cotton lint to the processor. Therefore, a system of premiums and discounts has been established to denote a specified base quality. In general, cotton fiber value increases as the bulk-averaged fibers increase in whiteness (+Rd), length, strength, and micronaire; and discounts are made for both low mike (micronaire less than 3.5) and high mike (micronaire more than 4.9).

Ideal fiber-quality specifications favored by processors traditionally have been summarized thusly: “as white as snow, as long as wool, as strong as steel, as fine as silk, and as cheap as hell.” These specifications are extremely difficult to incorporate into a breeding program or to set as goals for cotton producers. Fiber-classing technologies in use and being tested allow quantitation of fiber properties, improvement of standards for end-product quality, and, perhaps most importantly, creation of a fiber-quality language and system of fiber-quality measurements that can be meaningful and useful to producers and processors alike.
GENE AND ENVIRONMENTAL VARIABILITY:

Improvements in textile processing, particularly advances in spinning technology, have led to increased emphasis on breeding cotton for both improved yield and improved fiber properties  Studies of gene action suggest that, within upland cotton genotypes there is little non-additive gene action in fiber length, strength, and fineness ; that is, genes determine those fiber properties. However, large interactions between combined annual environmental factors (primarily weather) and fiber strength suggest that environmental variability can prevent full realization of the fiber-quality potential of a cotton genotype.
More recently, statistical comparisons of the relative genetic and environmental influences upon fiber strength suggest that fiber strength is determined by a few major genes, rather than by variations in the growth environment . Indeed, spatial variations of single fertility factors in the edaphic environment were found to be unrelated to fiber strength and only weakly correlated with fiber length .

Genetic potential of a specific genotype is defined as the level of fiber yield or quality that could be attained under optimal growing conditions. The degree to which genetic potential is realized changes in response to environmental fluctuations such as application of water or fertilizer and the inevitable seasonal shifts such as temperature, day length, and insolation.

In addition to environment-related modulations of fiber quality at the crop and whole-plant levels, significant differences in fiber properties also can be traced to variations among the shapes and maturities of fibers on a single seed and, consequently, within a given boll.

EFFECT ON FIBER LENGTH:

Comparisons of the fiber-length arrays from different regions on a single seed have revealed that markedly different patterns in fiber length can be found in the micropylar, middle, and chalazal regions of a cotton seed – at either end and around the middle . Mean fiber lengths were shortest at the micropylar (upper, pointed end of the seed) . The most mature fibers and the fibers having the largest perimeters also were found in the micropylar region of the seed. After hand ginning, the percentage of short fibers less than 0.5 inch or 12.7 mm long on a cotton seed was extremely low.

It has been reported that, in ginned and baled cotton, the short fibers with small perimeters did not originate in the micropylar region of the seed . MEasurements of fibers from micropylar and chalazal regions of seeds revealed that the location of a seed within the boll was related to the magnitude of the differences in the properties of fibers from the micropylar and chalazal regions.
Significant variations in fiber maturity also can be related to the seed position (apical, medial, or due to the variability inherent in cotton fiber, there is no absolute value for fiber length within a genotype or within a test sample . Even on a single seed, fiber lengths vary significantly because the longer fibers occur at the chalazal (cup-shaped, lower) end of the seed and the shorter fibers are found at the micropylar (pointed) end. Coefficients of fiber-length variation, which also vary significantly from sample to sample, are on the order of 40% for upland cotton.
Variations in fiber length attributable to genotype and fiber location on the seed are modulated by factors in the micro- and macroenvironment . Environmental changes occurring around the time of floral anthesis may limit fiber initiation or retard the onset of fiber elongation. Suboptimal environmental conditions during the fiber elongation phase may decrease the rate of elongation or shorten the elongation period so that the genotypic potential for fiber length is not fully realized . Further, the results of environmental stresses and the corresponding physiological responses to the growth environment may become evident at a stage in fiber development that is offset in time from the occurrence of the stressful conditions.
Fiber lengths on individual seeds can be determined while the fibers are still attached to the seed , by hand stapling or by photoelectric measurement after ginning. Traditionally, staple lengths have been measured and reported to the nearest 32nd of an inch or to the nearest millimeter. The four upland staple classes are: short (<21 mm), medium (22-25 mm), medium-long (26-28 mm) and long (29-34 mm). Pima staple length is classed as long (29-34 mm) and extra-long (>34 mm). Additionally, short fiber content is defined as the percentage of fiber less than 12.7 mm.

Cotton buyers and processors used the term staple length long before development of quantitative methods for measuring fiber properties. Consequently, staple length has never been formally defined in terms of a statistically valid length distribution.
In Fibrograph testing, fibers are randomly caught on combs, and the beard formed by the captured fibers is scanned photoelectrically from base to tip . The amount of light passing through the beard is a measure of the number of fibers that extend various distances from the combs. Data are recorded as span length (the distance spanned by a specific percentage of fibers in the test beard). Span lengths are usually reported as 2.5 and 50%. The 2.5% span length is the basis for machine settings at various stages during fiber processing.

The uniformity ratio is the ratio between the two span lengths expressed as a percentage of the longer length. The Fibrograph provides a relatively fast method for reproducibility in measuring the length and length uniformity of fiber samples. Fibrograph test data are used in research studies, in qualitative surveys such as those checking commercial staple-length classifications, and in assembling cotton bales into uniform lots.
Since 1980, USDA-AMS classing offices have relied almost entirely on high-volume instrumentation (HVI) for measuring fiber length and other fiber properties (Moore, 1996). The HVI length analyzer determines length parameters by photoelectrically scanning a test beard that is selected by a specimen loader and prepared by a comber/brusher attachment

The fibers in the test beard are assumed to be uniform in cross-section, but this is a false assumption because the cross section of each individual fiber in the beard varies significantly from tip to tip. The HVI fiber-length data are converted into the percentage of the total number of fibers present at each length value and into other length parameters, such as mean length, upper-half mean length, and length uniformity . This test method for determining cotton fiber length is considered acceptable for testing commercial shipments when the testing services use the same reference standard cotton samples.

All fiber-length methods discussed above require a minimum of 5 g of ginned fibers and were developed for rapid classing of relatively large, bulk fiber samples. For analyses of small fiber samples , fiber property measurements with an electron-optical particle-sizer, the Zellweger Uster AFIS-A2  have been found to be acceptably sensitive, rapid, and reproducible. The AFIS-A2 Length and Diameter module  generates values for mean fiber length by weight and mean fiber length by number, fiber length histograms, and values for upper quartile length, and for short-fiber contents by weight and by number (the percentages of fibers shorter than 12.7 mm). The AFIS-A2 Length and Diameter module also quantifies mean fiber diameter by number .

Although short-fiber content is not currently included in official USDA-AMS classing office reports, short-fiber content is increasingly recognized as a fiber property comparable in importance to fiber fineness, strength, and length . The importance of short-fiber content in determining fiber-processing success, yarn properties, and fabric performance has led the post-harvest sector of the U.S. cotton industry to assign top priority to minimizing short-fiber content, whatever the causes .
The perceived importance of short-fiber content to processors has led to increased demands for development and approval of a standard short-fiber content measurement that would be added to classing office HVI systems . This accepted classing office-measurement would allow inclusion of short-fiber content in the cotton valuation system. Documentation of post-ginning short-fiber content at the bale level is expected to reduce the cost of textile processing and to increase the value of the raw fiber . However, modulation of short-fiber content before harvest cannot be accomplished until the causes of increased short-fiber content are better understood.

Fiber length is primarily a genetic trait, but short-fiber content is dependent upon genotype, growing conditions, and harvesting, ginning, and processing methods. Further, little is known about the levels or sources of pre-harvest short-fiber content .

It is essential that geneticists and physiologists understand the underlying concepts and the practical limitations of the methods for measuring fiber length and short-fiber content so that the strong genetic component in fiber length can be separated from environmental components introduced by excessive temperatures and water or nutrient deficiencies. Genetic improvement of fiber length is fruitless if the responses of the new genotypes to the growth environment prevent full realization of the enhanced genetic potential or if the fibers produced by the new genotypes break more easily during harvesting or processing. The reported effects of several environmental factors on fiber length and short-fiber content, which are assumed to be primarily genotype-dependent, are discussed in the subsections that follow.
FIBER LENGTH AND TEMPERATURE:

Maximum cotton fiber lengths were reached when night temperatures were around 19 to 20 °C, depending on the genotype .  Early-stage fiber elongation was highly temperature dependent; late fiber elongation was temperature independent . Fiber length (upper-half mean length) was negatively correlated with the difference between maximum and minimum temperature.

Modifications of fiber length by growth temperatures also have been observed in planting-date studies in which the later planting dates were associated with small increases in 2.5 and 50% span lengths . If the growing season is long enough and other inhibitory factors do not interfere with fiber development, early-season delays in fiber initiation and elongation may be counteracted by an extension of the elongation period .

Variations in fiber length and the elongation period also were associated with relative heat-unit accumulations. Regression analyses showed that genotypes that produced longer fibers were more responsive to heat-unit accumulation levels than were genotypes that produced shorter fibers .  However, the earliness of the genotype was also a factor in the relationship between fiber length (and short-fiber content by weight) and accumulated heat units .

As temperature increased, the number of small motes per boll also increased. Fertilization efficiency, which was negatively correlated with small-mote frequency, also decreased. Although fiber length did not change significantly with increasing temperature, the percentage of short-fibers was lower when temperatures were higher. The apparent improvement in fiber length uniformity may be related to increased assimilate availability to the fibers because there were fewer seeds per boll.
FIBER LENGTH AND WATER:

Cotton water relationships and irrigation traditionally have been studied with respect to yield . Fiber length was not affected unless the water deficit was great enough to lower the yield to 700 kg ha-1. Fiber elongation was inhibited when the midday water potential was -2.5 to -2.8 mPa. Occurrence of moisture deficits during the early flowering period did not alter fiber length. However, when drought occurred later in the flowering period, fiber length was decreased .

Severe water deficits during the fiber elongation stage reduce fiber length ,  apparently due simply to the direct mechanical and physiological processes of cell expansion. However, water availability and the duration and timing of flowering and boll set can result in complex physiological interactions between water deficits and fiber properties including length.

FIBRE LENGTH AND LIGHT:

Changes in the growth environment also alter canopy structure and the photon flux environment within the canopy. For example, loss of leaves and bolls from unfavorable weather (wind, hail), disease, or herbivory and compensatory regrowth can greatly affect both fiber yield and quality . The amount of light within the crop canopy is an important determinant of photosynthetic activity  and, therefore, of the source-to-sink relationships that allocate photoassimilate within the canopy . Eaton and Ergle (1954) observed that reduced-light treatments increased fiber length. Shading during the first 7 d after floral anthesis resulted in a 2% increase in the 2.5% span length .

Shading (or prolonged periods of cloudy weather) and seasonal shifts in day length also modulate temperature, which modifies fiber properties, including length.

Commercial cotton genotypes are considered to be day-length neutral with respect to both flowering and fruiting . However, incorporation of day-length data in upland and pima fiber-quality models, based on accumulated heat units, increased the coefficients of determination for the length predictors from 30 to 54% for the upland model and from 44 to 57% for the pima model .

It was found that the light wavelengths reflected from red and green mulches increased fiber length, even though plants grown under those mulches received less reflected photosynthetic flux than did plants grown with white mulches. The longest fiber was harvested from plants that received the highest far red/red ratios.

FIBER LENGTH AND MINERAL NUTRITION:

Studies of the mineral nutrition of cotton and the related soil chemistry usually have emphasized increased yield and fruiting efficiency .  More recently, the effects of nutrient stress on boll shedding have been examined .  Also, several mineral-nutrition studies have been extended to include fiber quality .

Reports of fiber property trends following nutrient additions are often contradictory due to the interactive effects of genotype, climate, and soil conditions. Potassium added at the rate of 112 kg K ha-1yr-1 did not affect the 2.5% span length , when genotype was a significant factor in determining both 2.5 and 50% span lengths . Genotype was not a significant factor in Acala fiber length, but an additional 480 kg K ha-1yr-1 increased the mean fiber length .  K ha-1yr-1 increased the length uniformity ratio and increased 50%, but not 2.5% span length. Genotype and the interaction, genotype-by-environment, determined the 2.5% span length.

As mentioned above, fiber length is assumed to be genotype-dependent, but growth-environment fluctuations – both those resulting from seasonal and annual variability in weather conditions and those induced by cultural practices and inputs – modulate the range and mean of the fiber length population at the test sample, bale, and crop levels.
Quantitation of fiber length is relatively straightforward and reproducible, and fiber length (along with micronaire) is one of the most likely fiber properties to be included when cotton production research is extended beyond yield determinations. Other fiber properties are less readily quantified, and the resulting data are not so easily understood or analyzed statistically. This is particularly true of  fiber-breaking strength, which has become a crucial fiber property due to changes in spinning techniques.

FIBER STRENGTH:

The inherent breaking strength of individual cotton fibers is considered to be the most important factor in determining the strength of the yarn spun from those fibers . Recent developments in high-speed yarn spinning technology, specifically open-end rotor spinning systems, have shifted the fiber-quality requirements of the textile industry toward higher-strength fibers that can compensate for the decrease in yarn strength associated with open-end rotor spinning techniques.
Compared with conventional ring spinning, open-end rotor-spun yarn production capacity is five times greater and, consequently, more economical. Rotor-spun yarn is more even than the ring-spun, but is 15 to 20% weaker than ring-spun yarn of the same thickness. Thus, mills using open-end rotor and friction spinning have given improved fiber strength  highest priority. Length and length uniformity, followed by fiber strength and fineness, remain the most important fiber properties in determining ring-spun yarn strength.
Historically, two instruments have been used to measure fiber tensile strength, the Pressley apparatus and the Stelometer . In both of these flat-bundle methods, a bundle of fibers is combed parallel and secured between two clamps. A force to try to separate the clamps is applied and gradually increased until the fiber bundle breaks. Fiber tensile strength is calculated from the ratio of the breaking load to bundle mass. Due to the natural lack of homogeneity within a population of cotton fibers, bundle fiber selection, bundle construction and, therefore, bundle mass measurements, are subject to considerable experimental error .

Fiber strength, that is, the force required to break a fiber, varies along the length of the fiber, as does fiber fineness measured as perimeter, diameter, or cross section  Further, the inherent variability within and among cotton fibers ensures that two fiber bundles of the same weight will not contain the same number of fibers. Also, the within-sample variability guarantees that the clamps of the strength testing apparatus will not grasp the various fibers in the bundle at precisely equivalent positions along the lengths. Thus, a normalizing length-weight factor is included in bundle strength calculations.

In the textile literature, fiber strength is reported as breaking tenacity or grams of breaking load per tex, where tex is the fiber linear density in grams per kilometer . Both Pressley and stelometer breaking tenacities are reported as 1/8 in. gauge tests, the 1/8 in. (or 3.2 mm) referring to the distance between the two Pressley clamps. Flat-bundle measurements of fiber strength are considered satisfactory for acceptance testing and for research studies of the influence of genotype, environment, and processing on fiber (bundle) strength and elongation.

The relationships between fiber strength and elongation and processing success also have been examined using flat-bundle strength testing methods . However cotton fiber testing today requires that procedures be rapid, reproducible, automated, and without significant operator bias.  Consequently, the HVI systems used for length measurements in USDA-AMS classing offices are also used to measure the breaking strength of the same fiber bundles (beards) formed during length measurement.

Originally, HVI strength tests were calibrated against the 1/8-in. gauge Pressley measurement, but the bundle-strengths of reference cottons are now established by Stelometer tests that also provide bundle elongation-percent data. Fiber bundle elongation is measured directly from the displacement of the jaws during the bundle-breaking process, and the fiber bundle strength and elongation data usually are reported together (ASTM, 1994, D 4604-86). The HVI bundle-strength measurements are reported in grams-force tex-1 and can range from 30 and above (very strong) to 20 or below (very weak). In agronomic papers, fiber strengths are normally reported as kN m kg-1, where one Newton equals 9.81 kg-force .

The HVI bundle-strength and elongation-percent testing methods are satisfactory for acceptance testing and research studies when 3.0 to 3.3 g of blended fibers are available and the relative humidity of the testing room is adequately controlled. A 1% increase in relative humidity and the accompanying increase in fiber moisture content will increase the strength value by 0.2 to 0.3 g tex-1, depending on the fiber genotype and maturity.

Further, classing-office HVI measurements of fiber strength do not adequately describe the variations of fiber strength along the length of the individual fibers or within the test bundle. Thus, predictions of yarn strength based on HVI bundle-strength data can be inadequate and misleading . The problem of fiber-strength variability is being addressed by improved HVI calibration methods  and by computer simulations of bundle-break tests in which the simulations are based on large single-fiber strength databases of more than 20 000 single fiber long-elongation curves obtained with MANTIS .

Fiber Strength, Environment, and Genotype:

Reports of stelometer measurements of fiber bundle strength are relatively rare in the refereed agronomic literature. Consequently, the interactions of environment and genotype in determining fiber strength are not as well documented as the corresponding interactions that modulate fiber length. Growth environment, and genotype response to that environment, play a part in determining fiber strength and strength variability .

Early studies showed fiber strength to be significantly and positively correlated with maximum or mean growth temperature, maximum minus minimum growth temperature, and potential insolation . Increased strength was correlated with a decrease in precipitation. Minimum temperature did not affect fiber strength. All environmental variables were interrelated, and a close general association between fiber strength and environment was interpreted as indicating that fiber strength is more responsive to the growth environment than are fiber length and fineness. Other investigators reported that fiber strength was correlated with genotype only.

Square removal did not affect either fiber elongation  or fiber strength . Shading, leaf-pruning, and partial fruit removal decreased fiber strength . Selective square removal had no effect on fiber strength in bolls at the first, second, or third position on a fruiting branch . Fiber strength was slightly greater in bolls from the first 4 to 6 wk of flowering, compared with fibers from bolls produced by flowers opening during the last 2 wk of the flowering period .

In that study, fiber strength was positively correlated with heat unit accumulation during boll development, but genotype, competition among bolls, assimilatory capacity, and variations in light environment also helped determine fiber strength. Early defoliation, at 20% open bolls, increased fiber strength and length, but the yield loss due to earlier defoliation offset any potential improvement in fiber quality .

FIBER MATURITY:

Of the fiber properties reported by USDA-AMS classing offices for use by the textile industry, fiber maturity is probably the least well-defined and most misunderstood. The term, fiber maturity, used in cotton marketing and processing is not an estimate of the time elapsed between floral anthesis and fiber harvest . However, such chronological maturity can be a useful concept in studies that follow fiber development and maturation with time . On the physiological and the physical bases, fiber maturity is generally accepted to be the degree (amount) of fiber cell-wall thickening relative to the diameter or fineness of the fiber .

Classically, a mature fiber is a fiber in which two times the cell wall thickness equals or exceeds the diameter of the fiber cell lumen, the space enclosed by the fiber cell walls . However, this simple definition of fiber maturity is complicated by the fact that the cross section of a cotton fiber is never a perfect circle; the fiber diameter is primarily a genetic characteristic.

Further, both the fiber diameter and the cell-wall thickness vary significantly along the length of the fiber. Thus, attempting to differentiate, on the basis of wall thickness, between naturally thin-walled or genetically fine fibers and truly immature fibers with thin walls greatly complicates maturity comparisons among and within genotypes.

Within a single fiber sample examined by image analysis, cell-wall thickness ranged from 3.4 to 4.9 µm when lumen diameters ranged from 2.4 to 5.2 µm . Based on the cited definition of a mature fiber having a cell-wall thickness two times the lumen diameter, 90% of the 40 fibers in that sample were mature, assuming that here had been no fiber-selection bias in the measurements.

Unfortunately, none of the available methods for quantifying cell-wall thickness is sufficiently rapid and reproducible to be used by agronomists, the classing offices, or fiber processors. Fiber diameter can be quantified, but diameter data are of limited use in determining fiber maturity without estimates of the relationship between lumen width and wall thickness. Instead, processors have attempted to relate fiber fineness to processing outcome.
Estimating Fiber Fineness:

Fiber fineness has long been recognized as an important factor in yarn strength and uniformity, properties that depend largely on the average number of fibers in the yarn cross section. Spinning larger numbers of finer fibers together results in stronger, more uniform yarns than if they had been made up of fewer, thicker fibers . However, direct determinations of biological fineness in terms of fiber or lumen diameter and cell-wall thickness are precluded by the high costs in both time and labor, the noncircular cross sections of dry cotton fibers, and the high degree of variation in fiber fineness.

Advances in image analysis have improved determinations of fiber biological fineness and maturity , but fiber image analyses remain too slow and limited with respect to sample size for inclusion in the HVI-based cotton-classing process.

Originally, the textile industry adopted gravimetric fiber fineness or linear density as an indicator of the fiber-spinning properties that depend on fiber fineness and maturity combined . This gravimetric fineness testing method was discontinued in 1989, but the textile linear density unit of tex persists. Tex is measured as grams per kilometer of fiber or yarn, and fiber fineness is usually expressed as millitex or micrograms per meter . Earlier, direct measurements of fiber fineness (either biological or gravimetric) subsequently were replaced by indirect fineness measurements based on the resistance of a bundle of fibers to airflow.

The first indirect test method approved by ASTM for measurement of fiber maturity, lineardensity, and maturity index was the causticaire method. In that test, the resistance of a plug of cotton to airflow was measured before and after a cell-wall swelling treatment with an 18% (4.5 M) solution of NaOH (ASTM, 1991, D 2480-82). The ratio between the rate of airflow through an untreated and then treated fiber plug was taken as indication of the degree of fiber wall development. The airflow reading for the treated sample was squared and corrected for maturity to serve as an indirect estimate of linear density. Causticaire method results were found to be highly variable among laboratories, and the method never was recommended for acceptance testing before it was discontinued in 1992.

The arealometer was the first dual-compression airflow instrument for estimating both fiber fineness and fiber maturity from airflow rates through untreated raw cotton (ASTM, 1976, D 1449-58; Lord and Heap, 1988). The arealometer provides an indirect measurement of the specific surface area of loose cotton fibers, that is, the external area of fibers per unit volume (approximately 200-mg samples in four to five replicates). Empirical formulae were developed for calculating the approximate maturity ratio and the average perimeter, wall thickness, and weight per inch from the specific surface area data. The precision and accuracy of arealometer determinations were sensitive to variations in sample preparation, to repeated sample handling, and to previous mechanical treatment of the fibers, e.g., conditions during harvesting, blending, and opening. The arealometer was never approved for acceptance testing, and the ASTM method was withdrawn in 1977 without replacement.

The variations in biological fineness and relative maturity of cotton fibers that were described earlier cause the porous plugs used in air-compression measurements to respond differently to compression and, consequently, to airflow . The IIC-Shirley Fineness/Maturity Tester (Shirley FMT), a dual-compression instrument, was developed to compensate for this plug-variation effect (ASTM, 1994, D 3818-92). The Shirley FMT is considered suitable for research, but is not used for acceptance testing due to low precision and accuracy. Instead, micronaire has become the standard estimate of both fineness and maturity in the USDA-AMS classing offices.

Fiber Maturity and Environment:

Whatever the direct or indirect method used for estimating fiber maturity, the fiber property being as sayed remains the thickness of the cell wall. The primary cell wall and cuticle (together »0.1 µm thick) make up about 2.4% of the total wall thickness ( »4.1 µm of the cotton fiber thickness at harvest) . The rest of the fiber cell wall (»98%) is the cellulosic secondary wall, which thickens significantly as polymerized photosynthate is deposited during fiber maturation. Therefore, any environmental factor that affects photosynthetic C fixation and cellulose synthesis will also modulate cotton fiber wall thickening and, consequently, fiber physiological maturation

Fiber Maturity and Temperature and Planting Date:

The dilution, on a weight basis, of the chemically complex primary cell wall by secondary-wall cellulose has been followed with X-ray fluorescence spectroscopy. This technique determines the decrease, with time, in the relative weight ratio of the Ca associated with the pectin-rich primary wall . Growth-environment differences between the two years of the studies cited significantly altered maturation rates, which were quantified as rate of Ca weight-dilution, of both upland and pima genotypes. The rates of secondary wall deposition in both upland and pima genotypes were closely correlated with growth temperature; that is, heat-unit accumulation .

Micronaire (micronAFIS) also was found to increase linearly with time for upland and pima genotypes . The rates of micronaire increase were correlated with heat-unit accumulations . Rates of increase in fiber cross-sectional area were less linear than the corresponding micronaire-increase rates, and rates of upland and pima fiber cell-wall thickening  were linear and without significant genotypic effect .

Environmental modulation of fiber maturity (micronaire) by temperature was most often identified in planting- and flowering-date studies . The effects of planting date on micronaire, Shirley FMT fiber maturity ratio, and fiber fineness (in millitex) were highly significant in a South African study (Greef and Human, 1983). Although genotypic differences were detected among the three years of that study, delayed planting generally resulted in lower micronaire. The effect on fiber maturity of late planting was repeated in the Shirley FMT maturity ratio and fiber fineness data.

Planting date significantly modified degree of thickening, immature fiber fraction, cross-sectional area, and micronaire (micronAFIS) of four upland genotypes that also were grown in South Carolina . In general, micronaire decreased with later planting, but early planting also reduced micronaire of Deltapine 5490, a long-season genotype, in a year when temperatures were suboptimal during the early part of the season.

Harvest dates in this study also were staggered so that the length of the growing season was held constant within each year. Therefore, season-length should not have been an important factor in the relationships found between planting date and fiber maturity.
Fiber Maturity and Source-Sink Manipulation:

Variations in fiber maturity were linked with source-sink modulations related to flowering date , and seed position within the bolls . However, manipulation of source-sink relationships by early-season square (floral bud) removal had no consistently significant effect on upland cotton micronaire in one study . However, selective square removal at the first, second, and third fruiting sites along the branches increased micronaire, compared with controls from which no squares had been removed beyond natural square shedding . The increases in micronaire after selective square removals were associated with increased fiber wall thickness, but not with increased strength of elongation percent. Early-season square removal did not affect fiber perimeter or wall thickness (measured by arealometer) . Partial defruiting increased micronaire and had no consistent effect on upland fiber perimeter in bolls from August flowers.

Fiber Maturity and Water:

Generous water availability can delay fiber maturation (cellulose deposition) by stimulating competition for assimilates between early-season bolls and vegetative growth . Adequate water also can increase the maturity of fibers from mid-season flowers by supporting photosynthetic C fixation. In a year with insufficient rainfall, initiating irrigation when the first-set bolls were 20-d old increased micronaire, but irrigation initiation at first bloom had no effect on fiber maturity.  Irrigation and water-conservation effects on fiber fineness (millitex) were inconsistent between years, but both added water and mulching tended to increase fiber fineness. Aberrations in cell-wall synthesis that were correlated with drought stress have been detected and characterized by glycoconjugate analysis .

An adequate water supply during the growing season allowed maturation of more bolls at upper and outer fruiting positions, but the mote counts tended to be higher in those extra bolls and the fibers within those bolls tended to be less mature . Rainfall and the associated reduction in insolation levels during the blooming period resulted in reduced fiber maturity . Irrigation method also modified micronaire levels and distributions among fruiting sites.

Early-season drought resulted in fibers of greater maturity and higher micronaire in bolls at branch positions 1 and 2 on the lower branches of rainfed plants. However, reduced insolation and heavy rain reduced micronaire and increased immature fiber fractions in bolls from flowers that opened during the prolonged rain incident. Soil water deficit as well as excess may reduce micronaire if the water stress is severe or prolonged .
Fiber Maturity and Genetic Improvement:

Micronaire or maturity data now appear in most cotton improvement reports . In a five-parent half-diallel mating design, environment had no effect on HVI micronaire . However, a significant genotypic effect was found to be associated with differences between parents and the F1 generation and with differences among the F1 generation. The micronaire means for the parents were not significantly different, although HVI micronaire means were significantly different for the F1 generation as a group. The HVI was judged to be insufficiently sensitive for detection of the small difference in fiber maturity resulting from the crosses.
In another study, F2 hybrids had finer fibers (lower micronaire) than did the parents, but the improvements were deemed too small to be of commercial value.  Unlike the effects of environment on the genetic components of other fiber properties, variance in micronaire due to the genotype-by-environment interaction can reach levels expected for genetic variance in length and strength . Significant interactions were found between genetic additive variance and environmental variability for micronaire, strength, and span length in a study of 64 F2 hybrids .

The strong environmental components in micronaire and fiber maturity limit the usefulness of these fiber properties in studies of genotypic differences in response to growth environment. Based on micronaire, fiber maturity, cell-wall thickness, fiber perimeter, or fiber fineness data, row spacing had either no or minimal effect on okra-leaf or normal-leaf genotypes . Early planting reduced micronaire, wall-thickness, and fiber fineness of the okra-leaf genotype in one year of that study. In another study of leaf pubescence, nectaried vs. no nectaries, and leaf shape, interactions with environment were significant but of much smaller magnitude than the interactions among traits .
Micronaire means for Bt transgenic lines were higher than the micronaire means of Coker 312 and MD51ne when those genotypes were grown in Arizona . In two years out of three, micronaire means of all genotypes in this study, including the controls, exceeded 4.9; in other words, were penalty grade. This apparent undesirable environmental effect on micronaire may have been caused by a change in fiber testing methods in the one year of the three for which micronaire readings were below the upper penalty limit. Genotypic differences in bulk micronaire may either be emphasized or minimized, depending on the measurement method used .
GRADE:

In U.S. cotton classing, nonmandatory grade standards were first established in 1909, but compulsory upland grade standards were not set until 1915 . Official pima standards were first set in 1918. Grade is a composite assessment of three factors – color, leaf, and preparation . Color and trash (leaf and stem residues) can be quantified instrumentally, but traditional, manual cotton grade classification is still provided by USDA-AMS in addition to the instrumental HVI trash and color values. Thus, cotton grade reports are still made in terms of traditional color and leaf grades; for example, light spotted, tinged, strict low middling.
Preparation:

There is no approved instrumental measure of preparation – the degree of roughness/smoothness of the ginned lint. Methods of harvesting, handling, and ginning the cotton fibers produce differences in roughness that are apparent during manual inspection; but no clear correlations have been found between degree of preparation and spinning success. The frequency of tangled knots or mats of fiber (neps) may be higher in high-prep lint, and both the growth and processing environments can modulate nep frequency . However, abnormal preparation occurs in less than 0.5% of the U.S. crop during harvesting and ginning.

Trash or Leaf Grade:

Even under ideal field conditions, cotton lint becomes contaminated with leaf residues and other trash . Although most foreign matter is removed by cleaning processes during ginning, total trash extraction is impractical and can lower the quality of ginned fiber. In HVI cotton classing, a video scanner measures trash in raw cotton, and the trash data are reported in terms of the total trash area and trash particle counts (ASTM, D 4604-86, D 4605-86). Trash content data may be used for acceptance testing. In 1993, classer’s grade was split into color grade and leaf grade . Other factors being equal, cotton fibers mixed with the smallest amount of foreign matter have the highest value. Therefore, recent research efforts have been directed toward the development of a computer vision system that measures detailed trash and color attributes of raw cotton .

The term leaf includes dried, broken plant foliage, bark, and stem particles and can be divided into two general categories: large-leaf and pin or pepper trash . Pepper trash significantly lowers the value of the cotton to the manufacturer, and is more difficult and expensive to remove than the larger pieces of trash.Other trash found in ginned cotton can include stems, burs, bark, whole seeds, seed fragments, motes (underdeveloped seeds), grass, sand, oil, and dust. The growth environment obviously affects the amount of wind-borne contaminants trapped among the fibers. Environmental factors that affect pollination and seed development determine the frequency of undersized seeds and motes.

Reductions in the frequencies of motes and small-leaf trash also have been correlated with semi-smooth and super-okra leaf traits . Environment (crop year), harvest system, genotype, and second order interactions between those factors all had significant effects on leaf grade . Delayed harvest resulted in lower-grade fiber. The presence of trash particles also may affect negatively the color grade.

Fiber Color:

Raw fiber stock color measurements are used in controlling the color of manufactured gray, bleached, or dyed yarns and fabrics .  Of the three components of cotton grade, fiber color is most directly linked to growth environment. Color measurements also are correlated with overall fiber quality so that bright (reflective, high Rd), creamy-white fibers are more mature and of higher quality than the dull, gray or yellowish fibers associated with field weathering and generally lower fiber quality . Although upland cotton fibers are naturally white to creamy-white, pre-harvest exposure to weathering and microbial action can cause fibers to darken and to lose brightness.

Premature termination of fiber maturation by applications of growth regulators, frost, or drought characteristically increases the saturation of the yellow (+b) fiber-color component. Other conditions, including insect damage and foreign matter contamination, also modify fiber color.

The ultimate acceptance test for fiber color, as well as for finished yarns and fabrics, is the human eye. Therefore, instrumental color measurements must be correlated closely with visual judgment. In the HVI classing system, color is quantified as the degrees of reflectance (Rd) and yellowness (+b), two of the three tri-stimulus color scales of the Nickerson-Hunter colorimeter.

Fiber maturity has been associated with dye-uptake variability in finished yarn and fabric, but the color grades of raw fibers seldom have been linked to environmental factors or agronomic practices during production.
Other Environmental Effects on Cotton Fiber Quality:

Although not yet included in the USDA-AMS cotton fiber classing system, cotton stickiness is becoming an increasingly important problem . Two major causes of cotton stickiness are insect honeydew from whiteflies and aphids and abnormally high levels of natural plant sugars, which are often related to premature crop termination by frost or drought. Insect honeydew contamination is randomly deposited on the lint in heavy droplets and has a devastating production-halting effect on fiber processing.

The cost of clearing and cleaning processing equipment halted by sticky cotton is so high that buyers have included honeydew free clauses in purchase contracts and have refused cotton from regions known to have insect-control problems. Rapid methods for instrumental detection of honeydew are under development for use in classing offices and mills .

FIBER QUALITY OR FIBER YIELD?

Like all agricultural commodities, the value of cotton lint responds to fluctuations in the supply-and-demand forces of the marketplace.  In addition, pressure toward specific improvements in cotton fiber quality – for example, the higher fiber strength needed for today’s high-speed spinning – has been intensified as a result of technological advances in textile production and imposition of increasingly stringent quality standards for finished cotton products.

Changes in fiber-quality requirements and increases in economic competition on the domestic and international levels have resulted in fiber quality becoming a value determinant equal to fiber yield . Indeed, it is the quality, not the quantity, of fibers ginned from the cotton seeds that decides the end use and economic value of a cotton crop and, consequently, determines the profit returned to both the producers and processors.
Wide differences in cotton fiber quality and shifts in demand for particular fiber properties, based on end-use processing requirements, have resulted in the creation of a price schedule, specific to each crop year, that includes premiums and discounts for grade, staple length, micronaire, and strength . This price schedule is made possible by the development of rapid, quantitative methods for measuring those fiber properties considered most important for successful textile production . With the wide availability of fiber-quality data from HVI, predictive models for ginning, bale-mix selection, and fiber-processing success could be developed for textile mills .
Price-analysis systems based on HVI fiber-quality data also became feasible . Quantitation, predictive modeling, and statistical analyses of what had been subjective and qualitative fiber properties are now both practical and common in textile processing and marketing.

Field-production and breeding researchers, for various reasons, have failed to take full advantage of the fiber-quality quantitation methods developed for the textile industry. Most field and genetic improvement studies still focus on yield improvement while devoting little attention to fiber quality beyond obtaining bulk fiber length, strength, and micronaire averages for each treatment . Indeed, cotton crop simulation and mapping models of the effects of growth environment on cotton have been limited almost entirely to yield prediction and cultural-input management.

Plant physiological studies and textile-processing models suggest that bulk fiber-property averages at the bale, module, or crop level do not describe fiber quality with sufficient precision for use in a vertical integration of cotton production and processing. More importantly, bulk fiber-property means do not adequately and quantitatively describe the variation in the fiber populations or plant metabolic responses to environmental factors during the growing season. Such pooled or averaged descriptors cannot accurately predict how the highly variable fiber populations might perform during processing.

Meaningful descriptors of the effects of environment on cotton fiber quality await high-resolution examinations of the variabilities, induced and natural, in fiber-quality averages. Only then can the genetic and environmental sources of fiber-quality variability be quantified, predicted, and modulated to produce the high-quality cotton lint demanded by today’s textile industry and, ultimately, the consumer.

Increased understanding of the physiological responses to the environment that interactively determine cotton fiber quality is essential. Only with such knowledge can real progress be made toward producing high yields of cotton fibers that are white as snow, as strong as steel, as fine as silk, and as uniform as genotypic responses to the environment will allow.

Digg This

Bedford-cord


PLAIN FACE BEDFORDCORD

Bedford cord is the class of weaves produces the longitudinal warp lines in the cloth with fine sunken lines between.

The Bedford cord named after the town of Bedford in England. It is a heavy fabric with a length wise ribbed weave that reassembled corduroy.

METHOD OF CONSTRUCTION

· At interval pair of ends work in perfectly plain order with the picks, therefore these lifts are first indicated

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· The number of ends between the pair of plain end being varied according to the width of cord required.

· The next stage is consist of inserting marks (which indicating warp float) on the first and second picks of alternate cords and on the third and fourth picks of the other cords.

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· The object of arranging the marks of the cord ends in alternate order is chiefly to equalize the lift of the ends.

· The designs are completed by inserting plain weave on the cord ends, which join with the plain working of the pair of ends.

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STRUCTURE DETAIL

· The cord ends float over three picks and under one while the picks float in pairs on the back of one cord and interweave in plain order in the next cord.

 

DRAFTING AND DENTING

· The usual order of drafting is shown here

· The plain ends are being drawn on the healds of front and accordingly the lifting plan is maid.

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· In order to fully develop the sunken lines the plain should be separated by the slits of the reed

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· In some cases however the plain ends are dented accordingly to the type of fineness required.

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· Sometimes the plain ends are woven two per slit and cord ends are three or four per slit. The number of ends in the width of a cord has some influence upon the order of denting.

WADDED BEDFORD CORD

This structure contain thick wadding or padding ends which lie between the rib face cloth and the weft floats on the undersides the arrangement to give grater prominence to the cord.

METHOD TO INTRODUCE WADDING ENDS

· First we decide the place at which the wadding ends are introduced

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· The wadding ends are raised where the picks floats at the back shown in design and are left down where the picks interweave in plain order.

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· In order of interlacement of the picks and position of warps is shown here

DRAFTING AND DENTING

· Here the drawing (drafting) is done in same maner as before only after plain order healdshafts. The wadding ends are drawn and then the cord ends.

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· Here while denting is done, like 2 ends per slit the wadding ends being dented extra

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· The number of wadding ends to each cord may be varied according to requirement.

OTHER:

· The design may be arranged with an odd number of each (not including the wadding ends) to each cord but it is then necessary to reserve the marks of an alternate pairs of the plain ends in order that the plain weave will join correctly.

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SUITABLE WEAVING CONDITION

· Face warp= 30’s Cotton, 108 Ends per inch

· Wadding warp= 2/20’s cotton

· Weft = 36’s cotton, 84 picks per inch

TWILL FACED BEDFORD CORD

· It is an another modification of Bedford cord structure consist of the using warp twill instead of plain weave for the picks which inter weave on the face of the cord stripes.

· Thus the warp being brought more prominently on surface.

· The construction is same like a plain face but the introduction of twill weave in place of plain is take place.

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WHY CORDS ARE FORMED?

· The structure is formed due to the occurrence of force variations in structure.

· The plain order is highly compact structure here end 6-7 will force the yarn downward due to the plain order.

· And the other region contain plain or twill order with the warp floats which will not force but allow the other ends to move up.

· And this variation of force form the force forms the cord.

 

 

BEDFORD CORD ARRANGED WITH ALTERNATE PICKS

· Bedford cord are also made with alternate picks floating at the back, in which case the pairs of plain ends require to be indicated in the reverse order.

· Here we take an example of 10 end wide cord first the marks of the pairs of plain ends are indicated

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· Then the marks which cut with plain marks are inserted on the alternate horizontal spaces.

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· Afterwards plain weave is inserted on the blank horizontal spaces of the cords as indicated

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· But in this case plain does not join perfectly with the plain marks of the pair of ends

· Wadding ends also may introduce according to the requirement, this wadding ends are shown raised over the picks which floats at the back.

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APPLICATION:

· Fabric produced with these weave may be made in medium weight cotton or spun rayon fabrics for Dress wear, Sports-wear and ornamental trimming.

· In heavier qualities, It is suitable for Soft furnishing when produced with cotton yarns or for Suiting when made up of worsted yarns.

· Also used for shirting, coating, upholstery, uniforms etc.

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Automatic bale openers or pluckers


Modern mills now a day’s uses automatic bale openers or feeders in place of conventional hopper bale openers for more accurate mixing/ blending and also helps to eliminate mane power.

Modern bale pluckers can be mainly classified into two categories viz.

1. Moving bale type

The first generation bale opening machines were mostly stationary. Only the bales were moved either backward or forward or in a circle. The examples of these types of machines are Trutzschler multi-bale plucker, karousel-beater type opener by Rieter, etc.

2. Moving beater type

The second generation machines are of the travelling type i.e. they move past the bales of the layout and extract material from top to bottom. Travelling machines have the advantages that more bales can be processed as an overall unit, and thus better long-term blend is achieved. It should be noted however that these machines extract material only in batches, i.e. they can process only one, two or maximum three bales simultaneously. If long term blend is needed to be achieved, then mixing machines must be included downstream from the bale opener. These machines are completely electrically controlled and extract material evenly from all bales evenly, independently of varying bale density.

In concept, these machines are most commonly utilized now a day. Machines similar to uni-floc by Rieter are developed by various other manufactures viz. Optimix by Hargeth Hollingsworth, B12 by marzoli and blendomat by Trutzschler. The latest uni-floc provided in modern Rieter blow room line in place of the hopper bale opener is UNI-FLOC A-11.

UNIFLOC A-11

· OPERATIONAL PRINCIPLE

The A 11 UNI-floc processes cotton from all sources and manmade fibres in staple lengths of up to 65 mm. The bales being opened are placed lengthwise or crosswise on both sides of the bale opener, and the take-off unit can process up to four different assortments.

Reduction of the raw material into micro-tufts is assured by the patented double teeth on the take-off roller and the grid with closely set clamping rails. The unique geometry of the double teeth ensures the uniform treatment of the entire bale surface. Retaining rollers travelling with the take-off unit prevent bale layers from sloughing and ensure precise, controlled operation over the entire height of the bale. The A 11 UNI-floc still produces small tufts, even at maximum output of 1400 kg/h.

The take-off unit is lowered by a preselected or computed distance at each pass. Running-in and running-out programs compensate for the differing hardness of the bales over their cross section and ensure a uniform level of production. The fan incorporated in the swivelling tower extracts the opened tufts and feeds them into the tuft channel running between the guide rails. Transport to the following machine is pneumatic.

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Figure 1 OPERATIONAL PRINCIPLE OF UNI-FLOC A-11

· DISTINGUISHING FEATURES OF UNI-FLOC A-11

The UNI-floc is basically one type of opening which is most commonly utilized in place of “hopper bale openers”. The distinguishing features of UNI-floc are:

1. Bale opening into micro-tufts for effective cleaning and dust extraction.

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Figure narrow grid gauge for micro-tufts

Micro tufts are the basic requirement for the production of yarn quality. Trash and dust can only be removed from natural fibres gently and efficiently on the surface of the tufts.

The take-off unit of the UNI-floc is considered to be the “heart” of the system as it is responsible for micro tuft formation. The patented take-off roller and the grid design with small gaps between the clamping rails enables small fibre tufts ( micro tufts ) to be extracted. The twin-tooth profile ensures uniform, gentle and efficient extraction of the tufts, also irrespective of the take-off roller’s direction of rotation.

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Figure comparison of output and tuft size in modern machine(UNI-floc A-11) to conventional bale opener

2. Uniform take-off of bale lay-down by means of “BALE PROFILEING”

Bale Profiling guarantees totally uniform bale take-off. The height profile of the bale lay-down is precisely detected by light barriers and memorized. Scanning is performed at a constant speed of 9 m/min. Tufts are already taken off in the profiling phase. Continuous feeding of the subsequent machines is thus ensured from the outset.

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Figure uniform take-off of bale lay-down by means of bale profiling

During the subsequent passes the bales are opened at the preselected speed of travel and take-off depth. In the process the system automatically compensates for differences in height in the bale profile. Labour-intensive manual levelling is eliminated. After the required height range, take-off depth and speed of travel have been entered for each group of bales, take-off proceeds fully automatically.

3. Simultaneous processing of up to 4 assortment

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We can lay down as many as 130 bales in four groups on each side of the machine. This means that four assortments can be processed automatically at the preselected take-off speed and with the required production volume.

4. Patented, individually interchangeable double teeth on the opening roller

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Figure Patented, individually interchangeable double teeth

The double teeth enable maintenance intervals to be halved. The teeth are mounted individually. They can easily and quickly be replaced if required, without removing the take-off roller. This explains the exceptionally high operational readiness of the A 11Uni-floc

5. Processing of cotton from all sources and man-made fibres in staple lengths of up to 65 mm

 

6. Output of up to 1 400 kg/h (carded sliver)

7. Bale lay-down over a length of 7.2 to 47.2 meters

A bale lay-down of overall lengths of 7.2 to 47.2 meters and two take-off of unit lengths of 1 700 mm and 2 300 mm. The maximum version is capable of accommodating raw material up to 40 000 kg. This ensures flexible, economical and largely autonomous processing on UNI-floc A-11.

8. Take-off width selectable between 1 700 mm and 2 300 mm

 

9. Graphic interface for easy, intuitive operation at the control panel

The control panel is placed facing the extraction duct, providing a clear view and safety for operating the machine. Setting and control of the A 11 UNI-floc can easily be performed at the screen.

10. Interface to higher-level control and information systems available

In the interests of optimum monitoring of the installation as a whole, this modern machine control unit can be connected to the UNI-control or UNI-command control system. UNI-control and NI command also provide the interface to Reiter’s higher-level SPIDER web mill monitoring system.

 

11. Maximum yield due to optimized processes

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Diamond and Diapers


These Designs, from the point of view of their construction, can be regarded as the further development of twill weave.

DIAMONDS

Those that are symmetrical about their vertical and horizontal axes which can be produced with the aid of point draft and vertical waved twilled peg-plan.

DIAPERS

Those that are symmetrical about their diagonal axes, these are based on herringbone draft and vertical waved twilled peg-plan.

Ø Diamond is constructed on wavy twill while Diaper is constructed on herringbone twill.

DIAMOND WEAVE

Principle of Construction:

True diamond shapes converge into a vertex and for this reason most designs of this type can be constructed economically on the point draft basis. The structure may be developed in following two ways:

1. By employing a vertical waved twill or zigzag as the lifting plan in conjunction with the point draft.

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By indicating a diamond base and building up the design symmetrically on each side of the centre thread.

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· While this represents the same twill arrange to zigzag vertically.

· Two such repeats are given in each direction.

First method is most commonly employed to produce economical diamonds.

DIAPERS CONSTRUCTION

This class of weave will form cut effect or dice effect with the implementation of herringbone twill. This effect is used in ornamentation, shirting, etc.

Principle of Construction:

Ø The simplest weaves of this type are produced as a further development of the herringbone twill, in which the principle of opposing a warp float on the one side of the design by a weft float on the other is extended in both directions, i.e. horizontally and vertically.

Ø In this manner a design is formed in which the typical herringbone cut splits the design into four quarters, the diagonally opposite caters being similar.

Ø These structures are frequently employed as they are capable of forming large design repeats with considerable economy in the number of heald to be used.

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v Diapers can also be constructed on the herringbone draft basis provided that the twills from which they were originated fall into a certain specific category the characteristic of such twills are:-

o They are even sided

o Their repeat splits in two halves each of which is symmetrical within itself.

o The lifts in each of the two halves are diametrically opposite

v Even sided twill containing more than two lines of float which do not split in the manner indicae than two lines of float which do not split in the mannner self.

v twills from which they were originated falll ted above cannot be woven with the economical herring bone draft.

v Warp and weft faced twills can also be used to produce diapers on the herring bone reversal but owing to the very prominent quartering of the repeat a distinct check effect is produced and for this reason, such effects are frequently termed as “dice checks”.

v In additional to the herring bone based diapers many other diaper forms can be constructed without a preconceived base.

Difference

Diamond weave Diaper weave
It looks like a diamond It looks like a dice checks
It can be formed by two methods

o Baseline

o Wavy twill

It can be formed by using herring bone twill
Diamond is asymmetrical on both vertically and horizontally. It is only diagonally similar.
This is used for dress material and furnishing fabrics. This is used for dress materials.
This weave is produced with point draft It is not produced with point draft.
Diamond made from 3/3.1/2 Horizontal Waved Twill & Point Draft

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Diamond made from 3/3.1/2 Horizontal Waved Twill & Point Draft

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Advanced Fibre Information System (AFIS)


Introduction:-

Advanced Fibre Information System is based on the single fibre testing. There are two modules, one for testing the number of neps and the size of neps, while the other one is used for testing the length and the diameter. Both modules can be applied separately or together.

Among all physical properties of the cotton, fiber length varies the most within any one sample. There are two sources of variability;

1) Variability that comes from mixing cottons of various lengths

2) Variability that is biological in nature and exists within a sample of the same origin.

The same variety grown under different conditions, with lower or higher fertilizer doses, irrigation, or pest control, can produce various lengths. This is why fibre length is tested as an average of many fibres. Fibres also break during handling and processing thus, emphasizing the need for measurement of magnitude of the length variation. There are many different measurements of fibre length, including staple length, model length, mean length (aver-age length), 2.5% span length, effective length, upper quartile length, upper-half mean length, length uniformity index, length uniformity ratio, span length, short fibre contents and floating fibre length.

The AFIS test provides several length parameters deduced from individual fibre measurements. The main measurements include: the mean length, the length upper percentiles, the length CV%, and the Short Fibre Content (defined as the percentage of fibres less than 12.7 mm in length). Fibre length information is provided as a number or as weight-based data (by number/by weight). The length distribution by weight is determined by the weight-frequency of fibres in the different length categories, that is the proportion of the total weight of fibres in a given length category. The length distribution by number is given by the proportion of the total number of fibres in different length categories. The length parameters by weight and by number are computed from the two distributions accordingly. Once the AFIS machine determines the length distribution, the machine computes the length distribution by weight assuming that all fibres have the same fineness. Samples do not require any preparation and a result is obtained in 2-3 minutes. The results generally show a good correlation with other methods.

With the introduction of AFIS, it is possible to determine the average properties for a sample, and also the variation from the fibre to fibre. The information content in the AFIS is more. The spinning mill is dependent on the AFIS testing method, to achieve the optimum conditions with the available raw material and processing machinery. The AFIS-N module is dealt here and it is basically used for counting the number of neps and the size of neps. The testing time per sample is 3 minutes in AFIS-.N module.

This system is quick, purpose oriented and reproducible counting of neps in raw material and at all process stages of short staple spinning mill. It is thus possible, based on forecasts supervisory measures and early warning information to practically eliminate subsequent complaints with respect to finished product. The lab personnel are freed from the time consuming, delicate and unpopular, proceeding of nep counting. Personnel turnover and job rotation no more affects the results of the nep counting. The personnel responsible for quality can now at least deal with the unpopular neps in a more purpose-oriented manner than ever before.

AFIS -Working principle:-

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A fibre sample of approximately 500 mg is inserted between the feed roller and the feed plate of the AFIS-N instrument Opening rollers open the fibre assembly and separate off the fibres, neps, trash and dust. The trash particles and dust are suctioned off to extraction. On their way through the transportation and acceleration channels, the fibres and neps pass through the optical sensor, which determines the number and size of the neps.

The corresponding impulses are converted into electrical signals, which are then transmitted to a microcomputer for evaluation purposes. According to these analyses, a distinction is made between the single fibres and the neps. The statistical data are calculated and printed out through a printer. The measuring process can be controlled through a PC-keyboard and a screen.

Uster AFIS PRO- application report

Various HVI models available in market in present date are:-

· USTER® AFIS PRO 2

Optional Modules:

• Length and Maturity (L&M) Module to measure cotton fiber length and maturity, integrating results into the USTERÒ AFIS PRO 2.

• Trash (T) Module to measure the dust and trash content in cotton, integrating results into the USTERÒ AFIS PRO 2.

• USTERÒ AFIS AUTOJET (AJ) Module to measure up to 30 samples automatically, reducing idle operating time.

UPSUninterrupted Power Supply device to support the computer and monitor

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