Production Planning and Scheduling Software for the Textile Industry


As far as enterprise resource planning systems (ERP) are concerned, the textile industry may still be a manageable affair. But the moment you talk about developing a production planning and scheduling software for this industry, you are asking for a difficult task to be performed. Many a veteran has failed in attempting to achieve this feat.


Some of the unique challenges posed by the textile industry to any production planning and scheduling software vendor are discussed here. These challenges can be grouped as raw material concerns, manufacturing lead time, manufacturing constraints, orders, and inventory.

Raw material concerns involve high raw material costs and seasonal raw material procurement cycles. Cotton, for example, is a seasonal commodity; therefore, the availability and price will change throughout the year. High raw material cost is another issue. Raw material costs may constitute as high as 60 to 70 percent of the total costs.

Manufacturing lead time can also pose challenges. Manufacturing lead times can be excessive, sometimes more than two months. This is because the raw cotton production process, for example, has to go through many processes and most of them have huge lead time requirements. Looms in particular take the most lead time. A loom machine can make only 500 meters of fabric in a day whereas typical order lengths are in the range of 25,000 to 50,000 meters range.

Most of the manufacturing processes also have high setup times. Quality analysis time also runs high as the finished cloth needs to be manually inspected for defects. Extra lead times also result due to unavoidable generation of inventory in the form of extra meters than the ordered lengths.

Another challenge involving manufacturing is that different processing speeds occur at different work centers, which must also be included in the equation. Dyeing machines, warping machines, spinning machines run at speeds of 30,000 to 50,000 meters of yarn per day but looms run at 500 meters of fabric per day. Because of this, there may be only 2 to 5 dyeing machines, but there may be as many as 500 looming machines in the same plant.

Likewise there are different processing requirements on the same production line. For example, up until the dyeing process, the manufacturing process fits orders that are big and similar. But at looming, the manufacturing process fits many smaller and varied orders. This poses a real challenge as fitting these two diametrically opposite requirements is next to impossible to do.

Another issue is the unpredictable generation of second quality textile and the fact that variations in color and shade is only known after the fabric has been woven and finished (though they are caused back at the dyeing stage). This can result in a lot of rejected material being processed unnecessarily, thus adding to manufacturing costs and processing time.

These manufacturing constraints ultimately impact customer orders. Because the production rates are very low on looms, customer orders are broken into smaller sub-orders, and the sub-orders are distributed to many looms to reduce the lead time for individual orders. However, variations in the color or shade from the order can also emerge, which, as explained earlier, are detected at the end of the entire process.

Not surprisingly, inventory is another challenge faced by the textile industry because there is a high generation of extra finished products. In addition to extra material resulting from second quality and color shade variations, extra yarn moves through the entire production cycle. Up to dyeing stage, the work-in-process (WIP) is in yarn form and the length of this yarn is fixed at the yarn making stage. It cannot be cut as per order lengths. These extra meters travel through the production cycle and end up as excess inventory, which is later adjusted in the next planning cycle. Consequently, plant capacity is inefficiently utilized due to unavoidable generation of extra meters—more than the lengths ordered.

After going through these constraints, it is obvious that it is difficult to develop production planning and scheduling software for the textile industry. Only a veteran who has in-depth industry knowledge as well as knowledge of how to tackle these constraints in the implementation can develop a software for planning and scheduling for the textile industry.

Dyeing versus Looming

It is very important to understand the different requirements at the dyeing and looming processes so a suitable planning and scheduling software can be suggested. The dyeing and looming processes are the true bottlenecks in the entire production cycle of all textile plants. Both dyeing and looming have high setup time, high production time, and high change overtime, but looms are far slower than dyeing machines. Looming is more like a warehouse with a lot of WIP inventory called grey stock and this grey stock is on many looming machines, in small quantities. Dyeing machines, however produce long sets of warp (dyed yarn). One set of warp can be produced by one dyeing machine in one day but the warp can only be consumed by at least 50 looming machines in one day. To keep the ability to produce many kinds of fabric, the manufacturers generally install many kinds of looming machines. All of these looms are fed by only 2 to 5 dyeing machines. Due to these factors the dyeing area is always hard-pressed to feed the looms with small lengths and different types of dyed yarn for the next work orders in line.

So dyeing machines are better suited to produce big quantities of dyed yarn of the same type, (e.g. same color and same number of ends). For example, if the ordered length of fabric is 25,000 meters and the order has been broken into 10 work orders at 10 looms, then it will take 5 days to finish the WIP at looming. This is if all the work orders are done simultaneously and speed of looms are 500 meters of fabric woven per day. A single dyeing machine will produce 25,000 meters of warp in half a day.


These challenges in the textile industry can be met by conducting a profitable to promise analysis; grouping, breaking, and sequencing orders; and by routing WIPs. In the textile industry, orders are considered more like combinatorial meters rather than individual order meters, so the same type of orders can be grouped and sequenced to achieve production efficiency as well as reduce inventory creation. All WIPs can also considered the same way for the same purpose.

Profitable to Promise Analysis

Businesses in the textile industry mostly gets varied orders in terms of rate per meter, quantity, fabric type etc. Because of this, each order has to be evaluated on profitability, customer service levels and long and short term goals of the company. Profitable to promise analysis allows the business to find out if the particular order will be profitable to make by considering the costs of raw material, process, inventory, and other factors against the price the customer is willing to pay. Thus it can be seen that some orders will be a lot more profitable than other orders. This analysis is perfectly possible if you have the right software tool, which can provide you with this kind of information.

If raw material availability, machine capacity, and production lead time are known at the time of order taking, then it is possible to give a definite delivery date to the customer. This is known ascapable to promise. If we can also provide information about customer, production, inventory, stock out, material, and other overhead costs down to the item level, and then compare all incurred costs to the selling price, it will be possible to decide whether the incoming order should be taken and what priority it can be assigned, at the time the order is being taken. This functionality is very important for the textile industry.

In conjunction with above mentioned factors, a planning system that is also capable of grouping, breaking, and sequencing orders while it is doing total lead time calculations to determine a delivery date will solve many production planning problems. It will eliminate waste, reduce the generation of extra inventory, increase machine capacity utilization, increase customer service levels, eliminate stock out costs, and reduce production costs.

Grouping, Breaking, and Sequencing Orders

Grouping, breaking, and sequencing orders will also help to overcome textile production challenges. Group smaller orders at dyeing process. The same dyeing WIPs can be grouped so the generation of extra meters can be minimized. Break bigger orders into many smaller orders at dyeing, and sequence them with other orders. Loom areas typically have many kinds of loom machines which can produce different kinds of fabric but at very slow rates. If big orders of same material are continuously coming from dyeing, they will only go to a particular loom machine which can process it; other loom machines which cannot use these warps will go idle for want of material. Another way to minimize set up time is to sequence WIP orders with the same color at the dyeing process. This will minimize the set up time to change of color. Also, breaking individual orders into many parts will create many work orders for the same order at looming process. This will minimize lead times significantly at looming.

Also, look for already existing inventory in the form of extra meters at looms and finished stock in the inventory to allocate these meters against the matched fresh orders and plan for the remaining meters.


It is not possible or desirable to test all the raw material or all the final output from a production process because of time and cost constraints. Also many tests are destructive so that there would not be any material left after it had been tested. Because of this, representative samples of the material are tested. The amount of material that is actually tested can represent a very small proportion of the total output. It is therefore important that this small sample should be truly representative of the whole of the material. For instance if the test for cotton fibre length is considered, this requires a 20 mg sample which may have been taken from a bale weighing 250kg. The sample represents only about one eleven-millionth of the bulk but the quality of the whole bale is judged on the results from it.

The aim of sampling is to produce an unbiased sample in which the proportions of, for instance, the different fibre lengths in the sample are the same as those in the bulk. Or to put it another way, each fibre in the bale should have an equal chance of being chosen for the sample methods from the test lot.

• Test specimen: this is the one that is actually used for the individual measurement and is derived from the laboratory sample. Normally, measurements are made from several test specimens.

• Package: elementary units (which can be unwound) within each container in the consignment. They might be bump top, hanks, skeins, bobbins, cones or other support on to which have been wound tow, top, sliver, roving or yarn.

• Container or case: a shipping unit identified on the dispatch note, usually a carton, box, bale or other container which may or may not contain packages.

Fibre sampling from bulk

Zoning is a method that is used for selecting samples from raw cotton or wool or other loose fibre where the properties may vary considerably from place to place. A handful of fibres is taken at random from each of at least 40 widely spaced places (zones) throughout the bulk of the consignment and is treated as follows. Each handful is divided into two parts and one half of it is discarded at random; the retained half is again divided into two and half of that discarded. This process is repeated until about nix fibres remain in the handful (where n is the total number of fibres required in the sample and x is the number of original handfuls). Each handful is treated in a similar manner and the fibres that remain are placed together to give a correctly sized test sample containing n fibres. The method is shown diagrammatically in Fig. 1. It is important that the whole of the final sample is tested.


Fig:- Sampling by zoneing

Core sampling
Core sampling is a technique that is used for assessing the proportion of grease, vegetable matter and moisture in samples taken from unopened bales of raw wool. A tube with a sharpened tip is forced into the bale and a core of wool is withdrawn. The technique was first developed as core boring in which the tube was rotated by a portable electric drill. The method was then developed further to enable the cores to be cut by pressing the tube into the bale manually. This enables samples to be taken in areas remote from sources of power. The tubes for manual coring are 600mm long so that they can penetrate halfway into the bale, the whole bale being sampled by coring from both ends. A detachable cutting tip is used whose internal diameter is slightly smaller than that of the tube so that the cores will slide easily up the inside of the tube. The difference in diameter also helps retain the cores in the tube as it is withdrawn. To collect the sample the tube is entered in the direction of compression of the bale so that it is perpendicular to the layers of fleeces. A number of different sizes of nominal tube diameter are in use, 14, 15 and 18mm being the most common the weight of core extracted varying accordingly. The number of cores extracted is determined according to a sampling schedule and the cores are combined to give the required weight of sample. As the cores are removed they are placed immediately in an air-tight container to prevent any loss of moisture from them. The weight of the bale at the time of coring is recorded in order to calculate its total moisture content.

The method has been further developed to allow hydraulic coring by machine in warehouses where large numbers of bales are dealt with. Such machines compress the bale to 60% of its original length so as to allow the use of a tube which is long enough to core the full length of the bale.

Fibre sampling from combed slivers, rovings and yarn

One of the main difficulties in sampling fibres is that of obtaining a sample that is not biased. This is because unless special precautions are taken, the longer fibres in the material being sampled are more likely to be selected by the sampling procedures, leading to a length-biased sample. This is particularly likely to happen in sampling material such as sliver or yarn where the fibres are approximately parallel. Strictly speaking, it is the fibre extent as defined in Fig. 1.2 rather than the fibre length as such which determines the likelihood of selection. The obvious area where length bias must be avoided is in the measurement of fibre length, but any bias can also have effects when other properties such as fineness and strength are being measured since these properties often vary with the fibre length.


Fig 2.:- The meaning of extenet

There are two ways of dealing with this problem:
1 Prepare a numerical sample (unbiased sample).
2 Prepare a length-biased sample in such a way that the bias can be allowed for in any calculation.


Fig 3:- Selection of numerical sample

Numerical sample
In a numerical sample the percentage by number of fibres in each length group should be the same in the sample as it is in the bulk. In Fig.3, A and B represent two planes separated by a short distance in a sample consisting of parallel fibres. If all the fibres whose left-hand ends (shown as solid circles) lay between A and B were selected by some means they would constitute a numerical sample. The truth of this can be seen from the fact that if all the fibres that start to the left of A were removed then it would not alter the marked fibres. Similarly another pair of planes could be imagined to the right of B whose composition would be unaffected by the removal of the fibres starting between A and B. Therefore the whole length of the sample could be divided into such short lengths and there would be no means of distinguishing one length from another, provided the fibres
are uniformly distributed along the sliver. If the removal of one sample does not affect the composition of the remaining samples, then it can be considered to be a numerical sample and each segment is representative of the whole.

Length-biased sample
In a length-biased sample the percentage of fibres in any length group is proportional to the product of the length and the percentage of fibres of that length in the bulk. The removal of a length-biased sample changes the composition of the remaining material as a higher proportion of the longer fibres are removed from it.


Fig4 :- selection of tuft sample

If the lines A and B in Fig. 3 represent planes through the sliver then the chance of a fibre crossing these lines is proportional to its length. If, therefore, the fibres crossing this area are selected in some way then the longer fibres will be preferentially selected. This can be achieved by gripping the sample along a narrow line of contact and then combing away any loose fibres from either side of the grips, so leaving a sample as depicted in Fig. 4 which is length-biased. This type of sample is also known as a tuft sample and a similar method is used to prepare cotton fibres for length measurement by the fibrograph. Figure  5 shows the fibre length histogram and mean fibre length from both a numerical sample and a length-biased sample prepared from the same material.


Fig:5 Histogram of length based and numerical samples

By a similar line of reasoning if the sample is cut at the planes A and B the section between the planes will contain more pieces of the longer fibres because they are more likely to cross that section. If there are equal numbers of fibres in each length group, the total length of the group with the longest fibres will be greater than that of the other groups so that there will be a greater number of those fibres in the sample. Samples for the measurement of fibre diameter using the projection microscope are prepared in this manner by sectioning a bundle of fibres, thus giving a length-biased sample. The use of a length-biased sample is deliberate in this case so that the measured mean fibre diameter is then that of the total fibre length of the whole sample. If all the fibres in the sample are considered as being joined end to end the mean fibre diameter is then the average thickness of that fibre.

Random draw method
This method is used for sampling card sliver, ball sliver and top. The sliver to be sampled is parted carefully by hand so that the end to be used has no broken or cut fibres. The sliver is placed over two velvet boards with the parted end near the front of the first board. The opposite end of the sliver is weighed down with a glass plate to stop it moving as shown in Fig. 1.6. A wide grip which is capable of holding individual fibres is then used to remove and discard a 2mm fringe of fibres from the parted end. This procedure is repeated, removing and discarding 2mm draws of fibre until a distance equal to that of the longest fibre in the sliver has been removed. The sliver end has now been ‘normalised’ and any of the succeeding draws can be used to make up a sample as they will be representative of all fibre lengths. This is because they represent a numerical sample as described
above where all the fibres with ends between two lines are taken as the sample. When any measurements are made on such a sample all the fibres must be measured.


Fig 6:- The random Draw method

Cut square method
This method is used for sampling the fibres in a yarn. A length of the yarn being tested is cut off and the end untwisted by hand. The end is laid on a small velvet board and covered with a glass plate. The untwisted end of the yarn is then cut about 5mm from the edge of the plate as shown in Fig. 7. All the fibres that project in front of the glass plate are removed one by one with a pair of forceps and discarded. By doing this all the cut fibres are removed, leaving only fibres with their natural length. The glass plate is then moved back a few millimetres, exposing more fibre ends. These are then removed one by one and measured. When these have all been measured the plate is moved back again until a total of 50 fibres have been measured. In each case once the plate has been moved all projecting fibre ends must be removed and measured. The whole process is then repeated on fresh lengths of yarn chosen at random from the bulk, until sufficient fibres have been measured.


Fig7 :- The cut square method

Yarn sampling

When selecting yarn for testing it is suggested that ten packages are selected at random from the consignment. If the consignment contains more than five cases, five cases are selected at random from it. The test sample then consists of two packages selected at random from each case. If the consignment contains less than five cases, ten packages are selected at random from all the cases with approximately equal numbers from each case. The appropriate number of tests are then carried out on each package.

Fabric sampling

When taking fabric samples from a roll of fabric certain rules must be observed. Fabric samples are always taken from the warp and weft separately as the properties in each direction generally differ. The warp direction should be marked on each sample before it is cut out. No two specimens should contain the same set of warp or weft threads. This is shown diagrammatically in Fig. 8 where the incorrect layout shows two warp samples which contain the same set of warp threads so that their properties will be very similar. In the correct layout each sample contains a different set of warp threads so that their properties are potentially different depending on the degree of uniformity of the fabric. As it is the warp direction in this case that is being tested the use of the same weft threads is not important. Samples should not be taken from within 50mm of the selvedge as the fabric properties can change at the edge and they are no longer representative of the bulk.


Fig 8:- Fabric Sampling

The new generation of carpet weaving machines combines flexibility and productivity


For a long time, high production and high speed were the most important factors in carpet weaving. Recently, a new aspect in the production has become important: flexibility. Carpet weavers want to weave different styles, qualities,…and change quickly. Flexibility is as or even more) important as productivity. he developments in raw materials contribute positively to this new rend. New dying techniques, chemical compounds, treatments,…improve the quality and increase the choice of raw material.Another definite contributor are the new developments and technical improvements of the weaving machines. The use of electronics,microprocessors, networking,…help fulfilling the increasing
demand for more colours and flexibility in style. The carpet designers can now use their full creativity.The aim of Van de Wiele is to keep fulfilling, as much as possible, the demands of the carpet industry. The Van de Wiele machinery covers abroad range of applications and carpet styles (see table 1 & 2). This article will handle more in detail the different carpet weaving techniques and machines.

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Advanced Fibre Information System (AFIS)


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:-


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:-


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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Raw material represents about 50 to 70% of the production cost of a short-staple yarn. This fact is sufficient to indicate the significance of the raw material for the yarn producer. It is not possible to use a problem-free raw material always, because cotton is a natural fibre and there are many properties which will affect the performance. If all the properties have to be good for the cotton, the raw material would be too expensive. To produce a good yarn with these difficulties, an intimate knowledge of the raw material and its behaviour in processing is a must.

Fibre characteristics must be classified according to a certain sequence of importance with respect to the end product and the spinning process. Moreover, such quantified characteristics must also be assessed with reference to the following

  • what is the ideal value?
  • what amount of variation is acceptable in the bale material?
  • what amount of variation is acceptable in the final blend

Such valuable experience, which allows one to determine the most suitable use for the raw material, can only be obtained by means of a long, intensified and direct association with the raw material, the spinning process and the end product.

Low cost yarn manufacture, fulfilling of all quality requirements and a controlled fibre feed with known fibre properties are necessary in order to compete on the world’s textile markets. Yarn production begins with the rawmaterial in bales, whereby success or failure is determined by the fibre quality, its price and availability. Successful yarn producers optimise profits by a process oriented selection and mixing of the rawmaterial, followed by optimization of the machine settings, production rates, operating elements, etc. Simultaneously, quality is ensured
by means of a closed loop control system, which requires the application of supervisory system at spinning and spinning preparation, as well as a means of selecting the most suitable bale mix.

A textile fibre is a peculiar object. It has not truly fixed length, width, thickness, shape and cross-section. Growth of natural fibres or production factors of manmade fibres are responsible for this situation. An individual fibre, if examined carefully, will be seen to vary in cross-sectional area along it length. This may be the result of variations in growth rate, caused by dietary, metabolic, nutrient-supply, seasonal, weather, or other factors influencing the rate of cell development in natural fibres. Surface characteristics also play some part in increasing the variability of fibre shape. The scales of wool, the twisted arrangement of cotton, the nodes appearing at intervals along the cellulosic natural fibres etc.

Following are the basic characteristics of cotton fibre

  • fibre length
  • fineness
  • strength
  • maturity
  • Rigidity
  • fibre friction
  • structural features

The atmosphere in which physical tests on textile materials are performed. It has a relative humidity of 65 + 2 per cent and a temperature of 20 + 2° C. In tropical and sub-tropical countries, an alternative standard atmosphere for testing with a relative humidity of 65 + 2 per cent and a temperature of 27 + 2° C
may be used.

The “length” of cotton fibres is a property of commercial value as the price is generally based on this character. To some extent it is true, as other factors being equal, longer cottons give better spinning performance than shorter ones. But the length of a cotton is an indefinite quantity, as the fibres, even in a small random bunch of a cotton, vary enormously in length. Following are the various measures of length in use in different countries

  • mean length
  • upper quartile
  • effective length
  • Modal length
  • 2.5% span length
  • 50% span length

Mean length:
It is the estimated quantity which theoretically signifies the arithmetic mean of the length of all the fibres present in a small but representative sample of the cotton. This quantity can be an average according to either number or weight.

Upper quartile length:
It is that value of length for which 75% of all the observed values are lower, and 25% higher.

Effective length:
It is difficult to give a clear scientific definition. It may be defined as the upper quartile of a
numerical length distribution
eliminated by an arbitrary construction. The fibres eliminated are shorter than half the effective length.

Modal length:
It is the most frequently occurring length of the fibres in the sample and it is related to mean and median for skew distributions, as exhibited by fibre length, in the following way.

(Mode-Mean) = 3(Median-Mean)

Median is the particular value of length above and below which exactly 50% of the fibres lie.

2.5% Span length:
It is defined as the distance spanned by 2.5% of fibres in the specimen being tested when the fibres are parallelized and randomly distributed and where the initial starting point of the scanning in the test is considered 100%. This length is measured using “DIGITAL FIBROGRAPH”.

50% Span length:
It is defined as the distance spanned by 50% of fibres in the specimen being tested when the fibres are parallelized and randomly distributed and where the initial starting point of the scanning in the test is considered 100%. This length is measured using “DIGITAL FIBROGRAPH”.

The South India Textile Research Association (SITRA) gives the following empirical relationships to estimate the Effective Length and Mean Length from the Span Lengths.

Effective length = 1.013 x 2.5% Span length + 4.39
Mean length = 1.242 x 50% Span length + 9.78

Even though, the long and short fibres both contribute towards the length irregularity of cotton, the short fibres are particularly responsible for increasing the waste losses, and cause unevenness and reduction in strength in the yarn spun. The relative proportions of short fibres are usually different in cottons having different mean lengths; they may even differ in two cottons having nearly the same mean fibre length, rendering one cotton more irregular than the other.It is therefore important that in addition to the fibre length of a cotton, the degree of irregularity of its length should also be known. Variability is denoted by any one of the following attributes

  1. Co-efficient of variation of length (by weight or number)
  2. irregularity percentage
  3. Dispersion percentage and percentage of short fibres
  4. Uniformity ratio

Uniformity ratio is defined as the ratio of 50% span length to 2.5% span length expressed as a percentage. Several instruments and methods are available for determination of length. Following are some

  • Shirley comb sorter
  • Baer sorter
  • A.N. Stapling apparatus
  • Fibrograph

uniformity ration = (50% span length / 2.5% span length) x 100
uniformity index = (mean length / upper half mean length) x 100

The negative effects of the presence of a high proportion of short fibres is well known. A high percentage of short fibres is usually associated with,
– Increased yarn irregularity and ends down which reduce quality and increase processing costs
– Increased number of neps and slubs which is detrimental to the yarn appearance
– Higher fly liberation and machine contamination in spinning, weaving and knitting operations.
– Higher wastage in combing and other operations.
While the detrimental effects of short fibres have been well established, there is still considerable debate on what constitutes a ‘short fibre’. In the simplest way, short fibres are defined as those fibres which are less than 12 mm long. Initially, an estimate of the short fibres was made from the staple diagram obtained in the Baer Sorter method

Short fibre content = (UB/OB) x 100

While such a simple definition of short fibres is perhaps adequate for characterising raw cotton samples, it is too simple a definition to use with regard to the spinning process. The setting of all spinning machines is based on either the staple length of fibres or its equivalent which does not take into account the effect of short fibres. In this regard, the concept of ‘Floating Fibre Index’ defined by Hertel (1962) can be considered to be a better parameter to consider the effect of short fibres on spinning performance. Floating fibres are defined as those fibres which are not clamped by either pair of rollers in a drafting zone.

Floating Fibre Index (FFI) was defined as

FFI = ((2.5% span length/mean length)-1)x(100)

The proportion of short fibres has an extremely great impact on yarn quality and production. The proportion of short fibres has increased substantially in recent years due to mechanical picking and hard ginning. In most of the cases the absolute short fibre proportion is specified today as the percentage of fibres shorter than 12mm. Fibrograph is the most widely used instrument in the textile industry , some information regarding fibrograph is given below.

Fibrograph measurements provide a relatively fast method for determining the length uniformity of the fibres in a sample of cotton in a reproducible manner.

Results of fibrograph length test do not necessarily agree with those obtained by other methods for measuring lengths of cotton fibres because of the effect of fibre crimp and other factors.

Fibrograph tests are more objective than commercial staple length classifications and also provide additional information on fibre length uniformity of cotton fibres. The cotton quality information provided by these results is used in research studies and quality surveys, in checking commercial staple length classifications, in assembling bales of cotton into uniform lots, and for other purposes.

Fibrograph measurements are based on the assumptions that a fibre is caught on the comb in proportion to its length as compared to toal length of all fibres in the sample and that the point of catch for a fibre is at random along its length.


Fibre fineness is another important quality characteristic which plays a prominent part in determining the spinning value of cottons. If the same count of yarn is spun from two varieties of cotton, the yarn spun from the variety having finer fibres will have a larger number of fibres in its cross-section and hence it will be more even and strong than that spun from the sample with coarser fibres.

Fineness denotes the size of the cross-section dimensions of the fibre. AS the cross-sectional features of cotton fibres are irregular, direct determination of the area of croo-section is difficult and laborious. The Index of fineness which is more commonly used is the linear density or weight per unit length of the fibre. The unit in which this quantity is expressed varies in different parts of the world. The common unit used by many countries for cotton is micrograms per inch and the various air-flow instruments developed for measuring fibre fineness are calibrated in this unit.

Following are some methods of determining fibre fineness.

  • gravimetric or dimensional measurements
  • air-flow method
  • vibrating string method

Some of the above methods are applicable to single fibres while the majority of them deal with a mass of fibres. As there is considerable variation in the linear density from fibre to fibre, even amongst fibres of the same seed, single fibre methods are time-consuming and laborious as a large number of fibres have to be tested to get a fairly reliable average value.

It should be pointed out here that most of the fineness determinations are likely to be affected by fibre maturity, which is an another important characteristic of cotton fibres.

The resistance offered to the flow of air through a plug of fibres is dependent upon the specific surface area of the fibres. Fineness tester have been evolved on this principle for determining fineness of cotton. The specific surface area which determines the flow of air through a cotton plug, is dependent not only upon the linear density of the fibres in the sample but also upon their maturity. Hence the micronaire readings have to be treated with caution particularly when testing samples varying widely in maturity.

In the micronaire instrument, a weighed quantity of 3.24 gms of well opened cotton sample is compressed into a cylindrical container of fixed dimensions. Compressed air is forced through the sample, at a definite pressure and the volume-rate of flow of air is measured by a rotometer type flowmeter. The sample for Micronaire test should be well opened cleaned and thoroughly mixed( by hand fluffing and opening method). Out of the various air-flow instruments, the Micronaire is robust in construction, easy to operate and presents little difficulty as regards its maintenance.


Fibre maturity is another important characteristic of cotton and is an index of the extent of
development of the fibres. As is the case with other fibre properties, the maturity of cotton fibres varies not only between fibres of different samples but also between fibres of the same seed. The causes for the differences observed in maturity, is due to variations in the degree of the secondary thickening or deposition of cellulose in a fibre.

A cotton fibre consists of a cuticle, a primary layer and secondary layers of cellulose surrounding the lumen or central canal. In the case of mature fibres, the secondary thickening is very high, and in some cases, the lumen is not visible. In the case of immature fibres, due to some physiological causes, the secondary deposition of cellulose has not taken sufficiently and in extreme cases the secondary thickening is practically absent, leaving a wide lumen throughout the fibre. Hence to a cotton breeder, the presence of excessive immature
fibres in a sample would indicate some defect in the plant growth. To a technologist, the presence of excessive percentage of immature fibres in a sample is undesirable as this causes excessive waste losses in processing lowering of the yarn appearance grade due to formation of neps, uneven dyeing, etc.

An immature fibre will show a lower weight per unit length than a mature fibre of the same cotton, as the former will have less deposition of cellulose inside the fibre. This analogy can be extended in some cases to fibres belonging to different samples of cotton also. Hence it is essential to measure the maturity of a cotton sample in addition to determining its fineness, to check whether the observed fineness is an inherent characteristic or is a result of the maturity.


The fibres after being swollen with 18% caustic soda are examined under the microscope with suitable magnification. The fibres are classified into different maturity groups depending upon the relative dimensions of wall-thickness and lumen. However the procedures followed in different countries for sampling and classification differ in certain respects. The swollen fibres are classed into three groups as follows

  1. Normal : rod like fibres with no convolution and no continuous lumen are classed as “normal”
  2. Dead : convoluted fibres with wall thickness one-fifth or less of the maximum ribbon width are classed as “Dead”
  3. Thin-walled: The intermediate ones are classed as “thin-walled”

A combined index known as maturity ratio is used to express the results.

Maturity ratio = ((Normal – Dead)/200) + 0.70
N – % of Normal fibres
D – % of Dead fibres

Around 100 fibres from Baer sorter combs are spread across the glass slide(maturity slide) and the overlapping fibres are again separated with the help of a teasing needle. The free ends of the fibres are then held in the clamp on the second strip of the maturity slide which is adjustable to keep the fibres stretched to the desired extent. The fibres are then irrigated with 18% caustic soda solution and covered with a suitable slip. The slide is then placed on the microscope and examined. Fibres are classed into the following three categories

  1. Mature : (Lumen width “L”)/(wall thickness”W”) is less than 1
  2. Half mature : (Lumen width “L”)/(wall thickness “W”) is less than 2 and more than 1
  3. Immature : (Lumen width “L”)/(wall thickness “W”) is more than 2

About four to eight slides are prepared from each sample and examined. The results are presented as percentage of mature, half-mature and immature fibres in a sample. The results are also expressed in terms of “Maturity Coefficient”

Maturity Coefficient = (M + 0.6H + 0.4 I)/100 Where,

M is percentage of Mature fibres
H is percentage of Half mature fibres
I is percentage of Immature fibres

If maturity coefficient is

  • less than 0.7, it is called as immature cotton
  • between 0.7 to 0.9, it is called as medium mature cotton
  • above 0.9, it is called as mature cotton


There are other techniques for measuring maturity using Micronaire instrument. As the fineness value determined by the Micronaire is dependent both on the intrinsic fineness(perimeter of the fibre) and the maturity, it may be assumed that if the intrinsic fineness is constant then the Micronaire value is a measure of the maturity

Mature and immature fibers differ in their behaviour towards various dyes. Certain dyes are preferentially taken up by the mature fibres while some dyes are preferentially absorbed by the immature fibres. Based on this observation, a differential dyeing technique was developed in the United States of America for estimating the maturity of cotton. In this technique, the sample is dyed in a bath containing a mixture of two dyes, namely Diphenyl Fast Red 5 BL and Chlorantine Fast Green BLL. The mature fibres take up the red dye preferentially, while the thin walled immature fibres take up the green dye. An estimate of the average of the sample can be visually assessed by the amount of red and green fibres.

The different measures available for reporting fibre strength are

  1. breaking strength
  2. tensile strength and
  3. tenacity or intrinsic strength

Coarse cottons generally give higher values for fibre strength than finer ones. In order, to compare strength of two cottons differing in fineness, it is necessary to eliminate the effect of the difference in cross-sectional area by dividing the observed fibre strength by the fibre weight per unit length. The value so obtained is known as “INTRINSIC STRENGTH or TENACITY”. Tenacity is found to be better related to spinning than the breaking strength.

The strength characteristics can be determined either on individual fibres or on bundle of fibres.

The tenacity of fibre is dependent upon the following factorsclip_image004

chain length of molecules in the fibre orientation of molecules size of the crystallites distribution of the crystallites gauge length used the rate of loading type of instrument used and atmospheric conditions

The mean single fibre strength determined is expressed in units of “grams/tex”. As it is seen the the unit for tenacity has the dimension of length only, and hence this property is also expressed as the “BREAKING LENGTH”, which can be considered as the length of the specimen equivalent in weight to the breaking load. Since tex is the mass in grams of one kilometer of the specimen, the tenacity values expressed in grams/tex will correspond to the breaking length in kilometers.

In practice, fibres are not used individually but in groups, such as in yarns or fabrics. Thus, bundles or groups of fibres come into play during the tensile break of yarns or fabrics. Further,the correlation between spinning performance and bundle strength is atleast as high as that between spinning performance and intrinsic strength determined by testing individual fibres. The testing of bundles of fibres takes less time and involves less strain than testing individual fibres. In view of these  considerations, determination of breaking strength  of fibre bundles has assumed greater importance than single fibre strength tests.


There are three types of elongation

  • Permanent elongation: the length which extended during loading did not recover during relaxation
  • Elastic elongation:The extensions through which the fibres does return
  • Breaking elongation:the maximum extension at which the yarn breaks i.e.permanent and elastic elongation together Elongation is specified as a percentage of the starting length. The elastic elongation is of deceisive importance, since textile products without elasticity would hardly be usable. They must be able to deforme, In order to withstand high loading, but they must also return to shatpe. The greater resistance to crease
    for wool compared to cotton arises, from the difference in their elongation. For cotton it is 6 -10% and for wool it is aroun 25 – 45%. For normal textile goods, higher elongation are neither necessary nor desirable. They make processing in the spinning mill more difficult, especially in drawing operations.


The Torsional rigidity of a fibre may be defined as the torque or twisting force required to twist 1 cm length of the fibre through 360 degrees and is proportional to the product of the modulus of rigidity and square of the area of cross-section, the constant of proportionality being dependent upon the shape of the cross-section of the fibre. The torsional rigidity of cotton has therefore been found to be very much dependent upon the gravimetric fineness of the fibres. As the rigidity of fibres is sensitive to the relative humidity of the surrounding atmosphere, it is essential that the tests are carried out in a conditional room where the relative
humidity is kept constant.

Fibre stiffness plays a significant role mainly when rolling, revolving, twisting movements are involved. A fibre which is too stiff has difficulty adapting to the movements. It is difficult to get bound into the yarn, which results in higher hairiness. Fibres which are not stiff enough have too little springiness. They do not return to shape after deformation. They have no longitudinal resistance. In most cases this leads to formation of neps. Fibre stiffness is dependent upon fibre substance and also upon the relationship between fibre length and fibre fineness. Fibres having the same structure will be stiffer, the shorter they are. The slenderness ratio can serve as a measure of stiffness,

slender ratio = fibre length /fibre diameter

Since the fibres must wind as they are bound-in during yarn formation in the ring spinning machine, the slenderness ratio also determines to some extent where the fibres will finish up.fine and/or long fibres in the middle coarse and/or short fibres at the yarn periphery.

In addition to useable fibres, cotton stock contains foreign matter of various kinds. This foreign material can lead to extreme disturbances during processing. Trash affects yarn and fabric quality. Cottons with two different trash contents should not be mixed together, as it will lead to processing difficulties. Optimising process parameters will be of great difficulty under this situation, therefore it is a must to know the amount of trash and the type of trash before deciding the mixing.

A popular trash measuring device is the Shirley Analyser, which separates trash and foreign matter from lint by mechanical methods. The result is an expression of trash as a percentage of the combined weight of trash and lint of a sample. This instrument is used

  • to give the exact value of waste figures and also the proportion of clean cotton and trash in the material
  • to select the proper processing sequence based upon the trash content
  • to assess the cleaning efficiency of each machine
  • to determine the loss of good fibre in the sequence of opening and cleaning.

Stricter sliver quality requirements led to the gradual evolution of opening and cleaning machinery leading to a situation where blow room and carding machinery were designed to remove exclusively certain specific types of trash particles. This necessitated the segregation of the trash in the cotton sample to different grades determined by their size. This was achieved in the instruments like the Trash Separator and the Micro Dust Trash Analyser which could be considered as modified versions of the Shirley Analyser.

The high volume instruments introduced the concept of optical methods of trash measurement which utilised video scanning trash-meters to identify areas darker than normal on a cotton sample surface. Here, the trash content was expressed as the percentage area covered by the trash particles. However in such methods, comparability with the conventional method could not be established in view of the non-uniform distribution of trash in a given cotton sample and the relatively smaller sample size to determine such a parameter. Consequently, it is yet to establish any significant name in the industry.

Fineness determines how many fibres are present in the cross-section of a yarn of particular linear density. 30 to 50 fibres are needed minimum to produce a yarn fibre fineness influences

  1. spinning limit
  2. yarn strength
  3. yarn evenness
  4. yarn fullness
  5. drape of the fabric
  6. lustre
  7. handle
  8. productivity

productivity is influenced by the end breakage rate and twist per inch required in the yarn

Immature fibres(unripe fibres) have neither adequate strength nor adequate longitudinal siffness. They therefore lead to the following,

  1. loss of yarn strength
  2. neppiness
  3. high proportion of short fibres
  4. varying dyeability
  5. processing difficulties at the card and blowroom

Fibre length is one among the most important characteristics. It influnces

  1. spinning limit
  2. yarn strength
  3. handle of the product
  4. lustre of the product
  5. yarn hairiness
  6. productivity

It can be assumed that fibres of under 4 – 5 mm will be lost in processing(as waste and fly). fibres upto about 12 – 15 mm do not contribute to strength but only to fullness of the yarn. But fibres above these lengths produce the other positive characteristics in the yarn.

The proportion of short fibres has extremely great influence on the following parameters

  1. spinning limit
  2. yarn strength
  3. handle of the product
  4. lustre of the product
  5. yarn hairiness
  6. productivity

A large proportion of short fibre leads to strong fly contamination, strain on personnel, on the machines, on the work room and on the air-conditioning, and also to extreme drafting difficulties.

A uniform yarn would have the same no of fibres in the cross-section, at all points along it. If the fibres themeselves have variations within themselves, then the yarn will be more irregular.

If 2.5% span length of the fibre increases, the yarn strength also icreases due to the fact that
there is a greater contribution by the fibre strength for the yarn strength in the case of longer fibres.

Neps are small entanglements or knots of fibres. There are two types of neps. They are 1.fibre neps and 2.seed-coat neps.In general fibre neps predominate, the core of the nep consists of unripe and dead fibres. Thus it is clear that there is a relationship between neppiness and maturity index. Neppiness is also dependent on the fibre fineness, because fine fibres have less longitudinal stiffness than coarser fibres.

Nature produces countless fibres, most of which are not usable for textiles because of inadequate strength.

The minimum strength for a textile fibre is approximately 6gms/tex ( about 6 kn breaking length).

Since blending of the fibres into the yarn is achieved mainly by twisting, and can exploit 30 to 70% of the strength of the material, a lower limit of about 3 gms/tex is finally obtained for the yarn strength, which varies linearly with the fibre strength.

Low micronaire value of cotton results in higher yarn tenacity.In coarser counts the influence of micronaire to increase yarn tenacity is not as significant as fine count.

Fibre strength is moisture dependent. i.e. It depends strongly upon the climatic conditions and upon the time of exposure. Strength of cotton,linen etc. increases with increasing moisture content.

The most important property inflencing yarn elongation is fibre elongation.Fibre strength ranks seconds in importance as a contributor to yarn elongation. Fibre fineness influences yarn elongation only after fibre elongation and strength. Other characters such as span length, uniformity ratio, maturity etc, do not contribute significantly to the yarn elongation.Yarn elongation increases with increasing twist. Coarser yarn has higher elongation than finer yarn. Yarn elongation decreases with increasing spinning tension. Yarn elongation is also influenced
by traveller weight and high variation in twist insertion.

For ring yarns the number of thin places increases, as the trash content and uniformity ratio increased For rotor yarns 50%span length and bundle strength has an influence on thin places.

Thick places in ringyarn is mainly affected by 50%span length, trash content and shor fibre content.

The following expression helps to obtain the yarn CSP achievable at optimum twist multiplier with the available fibre properties.

Lea CSP for Karded count = 280 x SQRT(FQI) + 700 – 13C
Lea CSP for combed count = (280 x SQRT(FQI) + 700 – 13C)x(1+W)/100
L = 50% span length(mm)
S = bundle strength (g/tex)
M = Maturity ratio measured by shirly FMT
F = Fibre fineness (micrograms/inch)
C = yarn count
W = comber waste%

Higher FQI values are associated with higher yarn strength in the case of carded counts but in combed count such a relationship is not noticed due to the effect of combing

Higher 2.5 % span length, uniformity ratio, maturity ratio and lower trash content results in lower imperfection. FQI does not show any significant influence on the imperfection.

The unevenness of carded hosiery yarn does not show any significant relationships with any of the fibre properties except the micronaire value. As the micronaire value increases, U% also increases. Increase in FQI however shows a reduction in U%.

Honey-dew is the best known sticky substance on cotton fibres. This is a secretion of the cotton louse. There are other types of sticky substances also. They are given below.

  • honey dew – secretions
  • fungus and bacteria – decomposition products
  • vegetable substances – sugars from plant juices, leaf nectar, over production of wax,
  • fats, oils – seed oil from ginning
  • pathogens
  • synthetic substances – defoliants, insecticides, fertilizers, oil from harvesting machines

In the great majority of cases, the substance is one of a group of sugars of the most variable composition, primarily but not exclusively, fructose, glucose, saccharose, melezitose, as found, for example on sudan cotton. These saccharides are mostly, but not always, prodced by insects or the plants themselves, depending upon the influence on the plants prior to plucking. Whether or not a fibre will stick depends, not only on the quantity of the sticky coating and it composition, but also on the degree of saturation as a solution. Sugars are broken down by fermentation and by microorganisms during storage of the cotton. This occurs more quickly the higher the moisture content. During spinning of sticky cotton, the R.H.% of the air in the production are should be held as low as possible.

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