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1 AbouttheOriginandtheSignificanceoftheUSTER®STATISTICS

1.1 IntroductionThe USTER® STATISTICS are quality reference figures which permit a classification of fibers, slivers, rovings and yarns with regard to world production. The last USTER® STATISTICS for cotton fibers and yarns were published in 2001. The USTER® STATISTICS 2007 again address cotton fibers, rovings and yarns. We will later turn to the restrictions regarding the use of the USTER® STATISTICS. We recommend to read these restrictions carefully and adhere to them. When used properly the USTER® STATISTICS will continue to be appreciated as reference figures by all groups of interested people.

The USTER® STATISTICS are first and foremost a practical guide to ‘good textile practices’ in the field of yarn manufacturing. The evidence of specific defects or shortcomings in overall yarn quality, which may become apparent through using the STATISTICS as a comparative standard, can be translated into immediate corrective action in the manufacturing process. Reliable and unequivocal cause/effect relationships have been established over the years and documented in the application literature. Legions of textile technologists and USTER® instrument users in mills around the world put that experience into action in their daily routine. In the previous edition of the USTER® STATISTICS, we introduced a graph which illustrated the improvement in yarn evenness between 1957 and 2001. Now, six years later, we publish the same diagram again (Fig. 1). Two additional data points were added to the curves, i.e. the evenness values of the 50% line of the USTER® STATISTICS 2007. A further improvement in yarn evenness can be recognized in Fig. 1 for combed cotton yarns.

Fig.1 ImprovementinYarnEvennessbetween1957–2007

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As described in the USTER® STATISTICS 2001, the USTER® STATISTICS 2007 also distinguish between knitting yarns and weaving yarns for ring-spun yarns.

Of course, quality is multi-faceted, and while evenness has improved, other parameters have deteriorated to some degree. However, more than other quality parameters, yarn evenness is closely associated with both the design and management of the entire manufacturing process. Thus, besides being a result of technological advancements, evenness has also improved as a result of more elaborate quality control and quality management practices. It is of paramount importance for the spinning industry to closely monitor these trends and to prepare for a timely and appropriate response. Once lagging behind, a mill will have to invest heavily to move on and catch up and to eventually keep pace with the global development of yarn quality.

1.2 USTER® STATISTICS as BenchmarksThe USTER® STATISTICS have been made for quality benchmarking on the corporate level. Benchmarking is a total quality management tool and denotes the procedure of identifying and quantifying topnotch or world-class performance (benchmarks) in a particular business or product category and comparing the data with the performance of the own company or product. Established benchmarks and quality standards substantiate the feasibility of attaining greater proficiency and of narrowing the performance gap. They legitimize the implementation of strategies to enhance the manufacturing process as a result of hard facts rather than management intuition. In other industries, the availability of reliable competitor information for comparative analyses in benchmarking is a major obstacle. Thanks to the USTER® STATISTICS, data on the quality levels achieved by the top manufacturers in the textile industry are public domain and easily accessible.

1.3 USTER® STATISTICS for Yarn Contracts and Product SpecificationsThe USTER® STATISTICS regularly serve as the platform for yarn contracts and product specifications in the framework of commercial transactions. This practice is commonly accepted by the manufacturers, merchants, and processors of yarns. Many sales yarn spinners, weavers, knitters, and retailers have formulated quality requirements based on the USTER® STATISTICS. By experience they have determined what quality levels are appropriate for what application. As a more general guideline to the prevailing quality requirements, literature is available which addresses the subject of yarn specifications for an array of applications and end uses in both knitting and weaving. Much of the experience disclosed through these publications emanated from applying the USTER® STATISTICS.

Buyers and salesmen involved in the traditional commodity type trade or in direct purchasing and sales are certainly among the most enthusiastic users of the USTER® STATISTICS. They appreciate the STATISTICS as a means of categorizing many different qualities by face value. An indistinct yet popular belief prevails in the international markets for reasonably priced yarns that largely correspond to the 25th percentile of the USTER® STATISTICS to be in high demand. Every now and then, such a belief manifests an utter quality overkill with regard to the actual processing and end use requirements; in other cases, such specifications may well be justified. In the long run, however, the market as a whole is and will continue to be driven by the rule of supply and demand, irrespective of where, when, and by whom the STATISTICS are referred to in order to advertise or bargain. Good grades on the overall quality, though, will always serve as a passport to both the domestic and international markets.

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1.4 USTER® STATISTICS for Textile Machinery ManufacturersTextile machinery manufacturers as well as manufacturers of accessories for textile machines have frequently been using the USTER® STATISTICS to appraise the impact on quality of their new developments in the field of machine technology or monitoring and control systems. While machine performance in terms of productivity or efficiency is easily expressed in absolute numbers, the STATISTICS are frequently referred to when it comes to quality aspects. The other side of the coin is that the machinery manufacturers have also been forced into the routine of giving performance guarantees based on the USTER® STATISTICS. Again, this particular issue falls into the category of restrictive uses and will be addressed later.

1.5 Users’ Contributions Towards Improving the USTER® STATISTICSBecause of the constructive criticism that we received from among the industry, the USTER® STATISTICS for fiber and yarn quality have substantially improved over the years. We are invariably grateful for constructive contributions.

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2 QualityCharacteristicsoftheUSTER®STATISTICS 2007andTheirSignificance

The USTER® STATISTICS 2007 include all the quality characteristics which were published already in the USTER® STATISTICS 2001. In addition to this, we were able to contribute with some more quality characteristics of fibers and yarns in the USTER® STATISTICS 2007. For the first time we publish Foreign Fiber STATISTICS for the USTER® CLASSIMATQUANTUM.

The following list encompasses all quality characteristics featured in the USTER® STATISTICS 2007. It is subdivided into tables for fibers and tables for yarns.

All definitions of fiber quality characteristics which require an explanation are described in detail in Fig. 2 through 5.

Descriptionofcottonfiberqualitycharacteristics(USTER®HVI)

Quality Abbreviation DescriptionUnit characteristics

Micronaire Mic Indicates fiber fineness —-

Upper Half UHML Corresponds to the mm Mean Length classer's staple. Definition according to Fig. 2

Uniformity UI Measure for variations of % Index fiber length, length uniformity

Bundle Strength Breaking tenacity g/tex tenacity measured on fiber bundle

Reflexion Rd Degree of reflexion of % the cotton. The higher this value, the better the cotton is rated.

Yellowness +b Assessment of color, % degree of yellowness

Trash CNT Number of trash particles —- per defined area

Trash Area Percentage of trash % per defined area

Short Fiber SFI Amount of short fibers % Index calculated from the fibrogram

Spinnig SCI Calculated value based on —- Consistency a regression equation that Index takes into account all HVI properties

Maturity Mat Maturity of the cotton fibers —- Index (HVI method)

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Descriptionofcottonfiberqualitycharacteristics(USTER®AFIS)

Quality Abbreviation Description Unitcharacteristics

Neps Neps/g Number of neps 1/g per gram

Seed-coat SCN/g Number of seed-coat neps 1/g neps per gram

Short fiber SFC(n) Short fiber content % content SFC(w) by number (n) and by weight (w). Definition according to Fig. 3

Upper Quartile UQL(w) Corresponds to the mm Length classer's staple. Definition according to Fig. 3

Fiber fineness Fine Fineness of fibers mtex

Immature IFC Immature fiber content. % fibers Percentage of immature fibers. Definition according to Fig. �, Fig. 5

Maturity Mat Ratio of mature to immature fibers. —- Definition according to Fig. 5

Trash particles Trash/g Number of trash particles 1/g per gram

Dust particles Dust/g Number of dust particles 1/g per gram

Visible foreign VFM Visible foreign matter % matter

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The fiber length diagram determined by means of the USTER® HVI instrument is not an end-aligned staple diagram and is called fibrogram. Fig. 2 is a schematic fibrogram of cotton.

Fig.2 Fibrogram

The USTER® AFIS instrument measures each fiber separately and, therefore, all the information for an end-aligned staple diagram is available. Fig. 3 illustrates how the «Upper Quartile Length» (UQL) and the short fiber content are determined using the USTER® AFIS. The UQL is the fiber length at 25%. The term «upper quartile» indicates that the value is calculated at the upper quarter of the staple diagram.

Fig.3 Staplediagram

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Fig. � and Fig. 5 show the definition of the measured values in relation to the maturity characteristics. The respective parameters can be explained using Fig. �. Fig. � shows the cross-section of a cotton fiber.

Fig.4 Cottonfiber,cross-section

To compute the mean degree of thickening theta, a circular cross-section of the measured fiber having a perimeter P is calculated, and subsequently area A1 is divided by area A2.

Fig. 5 shows a maturity measurement using the USTER® AFIS as well as the values computed for theta.

Fig.5 MaturityHistogram

For this example, the following apply:

Mature fiber content R = 37.�%

Immature fiber content IFC = 10.3%

Maturity (according to Lord): M =R-IFC

200 +0.7 = 37.� - 10.3

200 +0.7 = 0.�

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Descriptionofyarnqualitycharacteristics(USTER®TESTER)

Quality Abbreviation Description Unitcharacteristics

Count variations CVcb Count variations between % packages

Mass variations CVm Coefficient of variation %

of mass

Mass variations CVmb Coefficient of variation %

of mass between packages

Imperfections Thin Number of thin places, 1/1000 m Thick thick places and neps Neps

Hairiness H Absolute value of hairiness. —- Measurement of the entire fiber length.

Standard deviation sH Standard deviation of hairiness —-

of hairiness within a package

Coefficient of CVHb Variation of hairiness %

variation between packages of hairiness

Trash Dust Dust and trash in yarns. 1/1000 m Trash Counts refer to 1000 m of yarn.

Coefficient of CVd Variation of the %

variation yarn diameter of the diameter

Shape Shape Shape of the yarn cross-section. —- Ratio of the axes of an ellipse.

Density D Density of the yarn g/cm3

Descriptionofyarnqualitycharacteristicsofrovings(USTER®TESTER)

Quality Abbreviation Description Unit characteristics

Count variation CVcb Count variations between % roving bobbins

Mass variation CVm Coefficient of variation of mass, %

cut length 1 cm

Mass variation CVm3m Coefficient of variation of mass, %

cut length 3 m

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Descriptionofyarnqualitycharacteristicsofslivers(SLIVERDATA,SLIVEREXPERT)

Quality Abbreviation Description Unitcharacteristics

Mass variation CVm Coefficient of variation of mass, %

cut length 2 cm

Mass variation CVm100m Coefficient of variation of mass, %

cut length 100 m

As various trials have shown, no improvements could be detected between the 2001 SLIVER STATISTICS and the unevenness of slivers we measure today. One explanation for this observation may be the increase of productivity in spinning preparation. For this reason we have taken over the sliver quality characteristics we have collected for the 2001 STATISTICS.

There exists a high correlation between the sliver measurement on the machines (SLIVERDATA, SLIVER EXPERT) and with the measurements in the laboratory (USTER® TESTER).

Descriptionofyarnqualitycharacteristics(USTER®TENSORAPID)

Quality Abbreviation Description Unitcharacteristics

Strength FH Breaking force cN

Tenacity RH Breaking force referred cN/tex

to the yarn count

Coefficient of CVRH Variation of the individual %

variation of values of the tenacity tenacity

Elongation εH Yarn elongation at % breaking force

Coefficient of CVεH Variation of the individual %

variation of elongation values elongation

Work done WH Work performed during cNcm

to break tensile testing of yarns at breaking force

Coefficient of CVWH Variation of the individual %

variation of values of work done work done to break

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Descriptionofyarnqualitycharacteristics(USTER®TENSOJET)

Quality Abbreviation Description Unitcharacteristics

Strength (Force) FH Breaking force cN

Tenacity RH Breaking force referred cN/tex

to the yarn count

Coefficient of CVRH Variation of the individual %

variation of values of the tenacity tenacity

Elongation εH Yarn elongation at % breaking force

Coefficient of CVεH Variation of the individual %

variation of elongation values elongation

Work done WH Work performed during cNcm to break tensile testing of yarns at breaking force

Coefficient of CVWH Variation of the individual %

variation of values of work done to work done break

Weak places FHP=0.1 0.1% of all tests cN

in the yarn / have a strength below strength this value

Weak places εHP=0.1 0.1% of all tests % in the yarn / have an elongation below elongation this value

Weak places FHP=0.01 0.01% of all tests cN

in the yarn / have a strength below strength this value

Weak places εHP=0.01 0.01% of all tests % in the yarn / have an elongation below elongation this value

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3 Restrictions

This section addresses the restrictions that apply to the use of the USTER® STATISTICS and we would like to repeat our advice that this be read carefully and adhered to. Both deliberate and unintentional misuse of the STATISTICS have in some instances in the past resulted in lengthy and costly disputes – all of which could have been avoided if all parties involved would have had the same clear understanding of the concept underlying the STATISTICS. The reading of this section is a must for those who are not familiar with that concept, with the STATISTICS as such, or with the proper interpretation of the data.

3.1 Restrictions Imposed by the Raw MaterialFour primary variables have a decisive impact on corporate success in our textile environment as well as in any other industrial venture: man, machine, material, and know-how or information in general. Among these four key elements, the raw material is the crucial component which largely dictates quality but also productivity and cost in yarn manufacturing. By virtue of their design, the USTER® STATISTICS for spun yarns do not provide direct access to information about the raw material used for spinning. However, those differences in raw material usage are indirectly reflected in the data. A high-quality yarn can only be spun from high-quality raw materials and since the raw material frequently accounts for more than 50% of the total manufacturing costs in the medium to fine count range, the utilization of high-quality, high-priced raw materials will be proportionally reflected in the yarn price. Any measures taken in the field of raw materials will not only have a considerable impact on quality but also on a mill’s competitiveness and bottom-line profitability.

In those rare cases where the STATISTICS have been corrupted, the motives have always been related to what evidently is the single most important driving force in the global textile scenario: price. The USTER® STATISTICS, however, provide a dependable indication of quality, exclusively. Although quality is a somewhat elusive term, it is nevertheless a result of tangible assets and thus to a certain degree interrelated with the sales price of a product.

The USTER® STATISTICS should not be interpreted as saying 5% is «good». In contrary, the 5% line might be indicative of high cost, high price, luxuriousness – even a tendency to price oneself out of the market. By the same token, 95% should not imply «poor» – it might be indicative of a very attractive price and just the right quality for the target markets. A «good» spinner is actually one who is in a position to achieve an acceptable quality level from a less expensive fiber – the genuine mastery of spinning. The trouble starts when the USTER® STATISTICS are referred to in order to corroborate complaints about a low rating in certain quality categories. This complaint may be directed at the «good» spinner who produces a reasonably priced yarn from a reasonably priced fiber. Yarn price, however, is directly proportional to fiber quality and fiber quality in turn dictates yarn quality to a great extent. Consequently, pushing yarn quality towards better values would simply cannibalize the price advantage. The USTER® STATISTICS should be employed as what they really are: a global survey of yarn quality as produced in every part of the world. Whether or not these qualities are produced economically from adequate raw materials and offered at a legitimate price is certainly beyond the scope of the STATISTICS.

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3.2 Restrictions Imposed by the Final ProductIt lies in the nature of the matter that end uses remain somewhat vague when yarns are marketed via merchants or importers. It is rare for any merchant to have firm orders before entering into a contract. Consequently, the focus is on obtaining qualities that are likely to meet the requirements of any potential customer and which can be successfully marketed in many places and at any given point in time. In the current buyer’s market, merchants have a large number of alternative sources to choose from. Yet, to minimize risk, commodity type yarns with high volume of trade are preferred. Under these circumstances, specified and actual quality requirements seem to have very little in common.

3.3 Restrictions Imposed by the Yarn DesignWhen properly tailored to the anticipated end use, yarns will exhibit inherent strengths and weaknesses: As opposed to weaving yarns, for instance, knitting yarns produced from cotton, man-made fibers, or blends thereof are spun at low twist multipliers. They will rarely display a high breaking tenacity. If they did, they would probably result in stiff, harsh fabrics. A somewhat lower breaking tenacity must also be expected from knitting yarns spun from low-tenacity or pill-resistant man-made fibers which are specifically designed for that purpose. Such low-tenacity fibers, however, usually result in excellent yarn elongation. Knitting yarns also possess a higher hairiness. While this would be detrimental to weaving yarns, the knitted fabric enjoys a greater cover and a softer hand. To make it clear: It is technically impossible and fatal with respect to the end use to demand that a yarn be perfect in all categories, say above the 25% line of the USTER® STATISTICS. The proper way out of this dilemma is for the yarn producer and the yarn processor to jointly develop detailed specifications or requirement profiles for specific end uses. Many good examples of this partnership approach have become known and the USTER® STATISTICS can be of tremendous help in realizing such projects.

3.4 Missing Correlation Between Different Quality CharacteristicsUnfortunately, the USTER® STATISTICS still mislead some people into thinking in causal relationships that do not exist in reality. Several quality parameters displayed in the STATISTICS are believed to be highly correlated but the fact is that they are not. High breaking tenacity, for instance, is not necessarily linked to high breaking elongation; rather, yarn elongation is determined by spinning speed, spinning geometry, and the resultant specific spinning tension. Likewise, a very even yarn may well have a high nep count. End uses calling for a relative freedom of neps cannot be satisfied by using yarns with a good USTER® CV. The opposite is sometimes the case: Few neps in a very uniform yarn tend to visually stick out like a black sheep. Yarns with a little higher CVm or greater hairiness tend to conceal neps in the overall irregularity, much like the often quoted needle in a haystack. If there is a problem with neppy appearance and no way to reduce nep counts, try to go a little higher with the CVm.

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3.5 Outliers and Frequent Defects in a Spinning MillIt is a popular illusion that yarns with a high rating according to the USTER® STATISTICS are always above and beyond suspicion. A good overall quality does not only encompass excellent mean values but also low variability of the quality attributes as well as unconditional consistency. Only one bad package in the creel of a knitting machine or in warping is bound to ruin several hundred yards of greige fabric. We have come a long way in gaining control over sporadic yarn defects by on-line quality monitoring and over scattered weak places by applying the USTER® TENSOJET. Every now and then, however, various off-quality situations tend to recur with malicious persistence in spite of the blind faith often put in the USTER® STATISTICS ratings. These include outliers, mix-ups, overlength/underlength or damaged packages, problems with package unwinding behavior, missing transfer tails, improper waxing, shedding and fly, dye streaks (barré), white specks, contamination with foreign fibers – just to name a few. Quality in a broader sense has many dimensions: A truckload of 5% USTER® STATISTICS yarn that arrives too late at the weaver’s loading ramp will not be considered a quality product. Timing is vital due to the seasonal characteristic of the textile business with its frequent peak demands and, of course, due to the increasing popularity of just-in-time and quick response production.

3.6 Restrictions in Guarantee AgreementsThe issue of performance guarantees negotiated between yarn producers and machinery manufacturers has already been briefly touched upon. Such performance guarantees based on the USTER® STATISTICS must be considered a dubious practice when the effect of raw material, machine settings, maintenance, ambient conditions, and operator proficiency is neglected. A legitimate performance guarantee should include references to in-depth technological trials conducted prior to preparing such a document. It should also embrace technically sound prohibitive clauses that serve to preclude misunderstandings – or even worse – litigation between machinery manufacturers and yarn producers. In the majority of all cases, it is not the machine that produces poor quality. If it would not have to process a capricious material like textile fibers, the average textile machine would probably run uninterruptedly for ten, fifteen years or more without any major problems at all. Before making claims against machinery manufacturers, the potential source of the quality problem as well as its true nature and extent should be investigated thoroughly and objectively.

3.7 Reproducibility and Variability of MeasurementsLast but not least, a few comments on reproducibility and variability of measurements. No matter what measuring instrument is used – from yardstick to atomic clock – there will always be a certain measurement error. This is also true for textile testing. There are three types of measurement errors: avoidable error, systematic error (bias), and random error. Avoidable error encompasses the failure to choose an appropriate measurement method or to properly operate a measuring instrument. In the textile laboratory, this is of little significance but selecting instrument settings and sample conditioning present a potential source of avoidable error. Systematic error includes calibration error, instrument tolerances, and the fluctuation of ambient conditions. This type of error can be quantified fairly accurately. Random error is the most critical component in textile testing. It is predominantly caused by the variability of the tested material itself. Its magnitude can be approximated by statistical calculations – the confidence interval of the mean. The absolute error of a measurement is the total of all three types of errors. A measurement should therefore always be reported as x±Dx, i.e. the mean value plus/minus the total error to indicate that the true measurement

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value is located somewhere within that interval. All USTER® instruments calculate the confidence intervals automatically and they are part of the test report. The confidence interval covers the random error component; information on the systematic error, i.e. instrument tolerances, is provided in our application handbooks.

When comparing actual measurements with the data illustrated in the USTER® STATISTICS, it is of utmost importance that the total measurement error is kept to an absolute minimum to warrant compatibility. If this is not the case, false conclusions may be drawn from such a comparison.

There are four items that can be done to minimize the measurement error:

• proper conditioning under constant standard atmospheric conditions• exact calibration of the instrument• correct settings of the instrument• adequate sample size

When actual measurements are then compared with the USTER® STATISTICS, they would appear in the nomogram as a short vertical line – not as a dot. The top and bottom ends of that line represent the upper and lower limits of the confidence interval with the mean exactly in the middle. We cannot eliminate the random error; however, the confidence interval quickly becomes smaller when the sample size is increased. For detailed information on recommended sample sizes and testing conditions, please refer to section 9.

In the context of commercial agreements via yarn contracts and product specifications, it frequently transpires that disputes result from discrepancies between measurements performed by the purchaser and by the supplier and from the subsequent comparison of disparate measurements with the USTER® STATISTICS. When such incidents are examined more closely, the result often is that the basic conditions listed above have been ignored or have simply not been identical in both testing locations. In other cases, the problem could be quickly resolved by applying the t-test procedure. It proved that the differences were not statistically significant but strictly random due to a pronounced sample variability. The t-test procedure along with further detailed explanations is outlined in our application handbooks. A simplified t-test can be performed by comparing the confidence intervals: If the confidence intervals of two means overlap, then the observed difference between the two means is random or statistically insignificant; if they are separated, the difference is considered statistically significant. Applying the concept of the confidence interval can be both very helpful and revealing. It pinpoints the highly variable characteristic of textile materials which should always be taken into consideration.

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4 TheMakingoftheUSTER® STATISTICS

The USTER® STATISTICS are not established by merely collecting data. They are established by testing actual yarn and fiber samples that we procure on a truly global scale via our agents, overseas partners, or direct contacts with our international clientele. Several thousand samples have been tested in our ISO 9001 certified textile laboratory in Uster, Switzerland. The geographical distribution of the origin of all samples procured for the USTER® STATISTICS is illustrated in Fig. �. The total volume of samples was tested between spring 2002 and 200�.

North&SouthAmerica

Europe

Asia&Oceania

17%

20%

51%Africa

12%

Fig.6 GeographicaldistributionoftheoriginofallsamplesprocuredfortheUSTER® STATISTICS2007

All data were entered into a databank and application software specifically developed for this purpose was employed to compute the percentile curves and to plot the graphical representations. The lion’s share of the total time spent was definitely devoted to thoroughly testing the samples in the laboratory. Our databank has grown to an enormous size and consists of far more quality parameters than have been published in this edition of the USTER® STATISTICS.

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5 InterpretingandApplyingtheUSTER®STATISTICS

The USTER® STATISTICS manual consists of several parts, each addressing a specific quality aspect in the sequence from fiber to yarn. The different sections are arranged according to spinning system and raw material composition or yarn style. Each section is subdivided into distinct quality attributes (e.g. mass variation, tensile properties, etc.) which are measured with different USTER® instruments. A measurement can consist of several individual parameters. Mass variation, for instance, includes CVm and the between-sample variation CVmb. These parameters are presented in graphical form. The origin of the samples processed to establish the raw data is illustrated by a pie chart. These pie charts are provided with each quality attribute but not with each parameter because the measurements were performed simultaneously on the same samples. A register is provided for quick reference to the sections of interest and after leafing through the pages a couple of times, you will find it easy to work with the USTER® STATISTICS.

The most important element of the USTER® STATISTICS are the nomograms with the percentile curves. The width of the percentile curves intentionally imposes certain restrictions on accuracy – a subtle reminder of the pronounced variability of most textile measurements. Depending on the quality parameter displayed on the ordinate (vertical or y-axis), the curves are plotted over staple length, process stage, yarn count, or defect category and the abscissa (horizontal or x-axis) is calibrated accordingly. The x-axis should be the starting point of any analysis. The percentile curves refer to the percentage of the total world production which equals or exceeds the measurement value given for a particular yarn or fiber description. An example:

The coefficient of variation of the yarn mass of an Ne 30 (Nm 50, 20 tex) 100% combed cotton ring-spun yarn for knitted fabrics has an evenness of CVm = 13%. A vertical line drawn from the x-axis at Ne 30 intersects with the horizontal line drawn from the y-axis at 13% right at the 75th percentile line. Hence, 75% of all Ne 30 combed cotton ring-spun yarns produced worldwide have a CVm of 13.0% or better. Vice versa, 25% of the total world production of comparable Ne 30 yarns exhibit a CVm higher than 13%.

The 50th percentile curve, commonly referred to as the 50% line, corresponds to the median. In general terms, the median is the middle number when the measurements in a data set are arranged in ascending (or descending) order, i.e. 50% of all observations exceed this value and the other 50% lie below. Depending on whether the frequency distribution of a given quality parameter is symmetric or skewed, the median may or may not be different from the mean.

In some instances, adjacent percentile curves fell very close together. To avoid the formation of a solid red block, both the 25% line and 75% line were omitted, thus maintaining the clarity of the illustration.

The nomograms in the fiber properties section as well as the ones in the fiber-to-yarn and yarn quality sections for combed cotton ring-spun yarns comprise two independent sets of percentile curves. The two sets of curves each characterize a distinct cluster or isolated population within the same graph. We will look at the cotton fiber properties first to explain the reasons for this differentiation: The horizontal position of the split point at a staple length of 30...31 mm marks the approximate center of the transition zone from both short and medium-staple cottons on one hand to long and extra long-staple cottons on the other. With that transition, several factors change fundamentally. These factors include genetic, botanical, and physiological differences, agricultural methods, environmental influences, harvesting and ginning practices, all of which have a decisive impact on fiber properties. On the yarn side, things are much simpler. Here, the division between Ne �1 (Nm 70, 1� tex) and Ne �7 (Nm �0, 12.5 tex) indicates the yarn count threshold for using longer staple, high-grade cottons with an overall superior fiber quality, for increasing comber noil extraction, and for modifying the overall processing conditions accordingly. Selecting higher quality cotton fibers and adjusting the processing conditions is necessary to raise the spin limit towards the finer counts. Naturally, in the fiber-to-yarn nomograms for combed roving, the two clusters occur as well. The curves had to be split at exactly the same position on the yarn count axis. The graphs provide an opportunity to study these effects of raw material selection and processing.

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6 ChangesandImprovements

6.1 New Fiber Quality CharacteristicsThree new quality characteristics were introduced with the USTER® STATISTICS 2007: The Short Fiber Index, the Spinning Consistency Index for USTER® HVI Systems, and the Fiber Fineness for USTER® AFIS Systems.

6.2 Distinction Between Knitting and Weaving YarnsIn the USTER® STATISTICS 2007 a distinction was made again between weaving and knitting yarns. The threshold between weaving and knitting yarn has been determined to be the following twist multipliers:

• Combed cotton yarn ae = 3.7 (am = 112) • Carded cotton yarn ae = 3.9 (am = 119)

Yarns with twist multipliers below these values have been classified as knitting yarns.

6.3 New Yarn Quality CharacteristicsThe two optical sensors OM and OI were used in the tests using the USTER® TESTER 4. They record the following quality characteristics: Variation of the yarn diameter, shape of the yarn cross-section, yarn density and the number of dust and trash particles in the yarn.

When the first USTER® STATISTICS for imperfections were published in 1957, a decision was taken after prolonged testing to define the following thresholds: thins –50%, thicks +50%, neps +200%. These values refer to the mean number of fibers in a cross-section of a yarn.

As explained in Fig. 1, the mass unevenness improved to such an extent in the past �0 years that often no imperfection counts can be found in combed cotton yarns in the middle and coarse range. Therefore, a decision was taken to include the next lower thresholds, i.e. the settings: thins –�0%, thicks +35%, neps +1�0% and for rotor yarns and airjet yarns +200%.

In addition, not only the tenacity but also the strength of yarns were determined during the tensile tests for the USTER® STATISTICS.

Nowadays, variations of quality characteristics are increasingly determined using the coefficient of variation CV. Therefore, the unevenness U is not published in the USTER® STATISTICS 2007 anymore. The conversion factor CV = 1.25 U can be used here for mass variations with normal distribution.

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6.4 The USTER® CLASSIMAT QUANTUM SystemThe appearance of a fault in the finished product, i.e. a woven or knitted fabric, is largely determined by its size. A yarn fault classification according to cross-section and length is therefore the basis for the assessment of yarn faults. By classification we understand the arrangement of yarn faults within a schematic (CLASSIMAT matrix) according to the fault data.

Tried and tested for many years, the classification matrix consists of 23 classes for the classification of "normal" thick and thin places. For a better assessment of yarn types with different structures, such as ply yarns or compact yarns, the classification matrix was extended by � additional classes. Of course, the extended classes can also be used for conventional yarns and can even provide additional helpful information.

Seldom-occurring yarn faults are categorized in the classification matrix of the USTER® CLASSIMATQUANTUM (Fig. 7). The classification begins at a length of 0.1 cm and is basically open in length.

Thick places are counted if the mean mass of a yarn is exceeded by at least 100% in the case of short faults or by �5% in the case of faults over � cm. With thin places, however, the actual value must be at least –30% below the mean value. The positive and the negative range of the top and bottom classes are open as well.

In the yarn fault classification, the yarn faults are entered in the CLASSIMAT matrix according to the fault length and fault cross-section. The faults are identified with a letter and a number. The letters stand for the fault length and the number indicates the deviation of the cross-section in comparison to the perfect yarn. Fig. 7 shows the standard matrix with 23 classes. For ply yarns and compact yarns there exist classes between +75% and +100%.

Fig.7 Classificationmatrixfordisturbingthickandthinplaces

Disturbing thick and thin places up to Ne �0 were measured with the capacitive clearer USTER® QUANTUM C20. The thick and thin places from Ne �1 to Ne 170 were measured with the clearer USTER® QUANTUM C15. However, it is not required that the CLASSIMAT operator has to switch over to a different measuring head at Ne �0.

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The USTER® CLASSIMATQUANTUM is also in a position to classify foreign fibers. Therefore, this instrument cannot only detect the remaining and disturbing thick and thin places but also the remaining foreign fibers.

Length

20%

10%

7%

5%0 1.0 1.4 2.0 3.0

Intensity

A3 B3 C3 D3

C2 D2

B14 C12 D12

C11

B22

B12

B13

B21

B11 D11

cm

nocounts

A2

30%

5.0 7.0

A4 B4 C4 D4

E3

E2

E12

E4

F

E11

Fig.8 Classificationmatrixfordisturbingforeignfibers

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7 Validity

The information provided with this edition supersedes all the descriptions pertaining to yarn quality published in previous editions of the USTER® STATISTICS. The quality of industrially manufactured goods is a moving target. It depends on a multitude of factors, most of which are an intrinsic function of time. The dependence on time is predominantly related to the state of technology of the productive assets and the technological know-how prevalent in the industry. Time is also a factor in determining the overall economic environment, the supply and demand situation, as well as general consumer attitudes and behavior. All of the above, acting jointly or separately, may have an effect on the quality of raw materials, semi-processed, or finished textile goods. Consequently, the validity of the information provided in the USTER® STATISTICS 2007 is confined to the period of time actually covered by the data. The data are essentially of historical nature by the time this document is published. Naturally, such information will not sustain its initial significance as time progresses and eventually become obsolete unless it is updated at some point in the future. Therefore, the information presented in this document in either verbal, numerical, or graphical form is subject to change at any time without prior or public notice. Conventional wisdom proves, however, that the USTER® STATISTICS maintain their significance over an extended period of five years or more.

With no exceptions, all the information provided in the USTER® STATISTICS 2007 relates to data which have been established using USTER® products. USTER® products are designed, manufactured, and distributed by Uster Technologies, Switzerland, and Uster Technologies Inc., USA, or authorized licensees, exclusively. Any attempt to utilize the information provided in this document in conjunction with data originating from sources other than USTER® instruments may result in some form of failure or damage. The USTER® STATISTICS are intended for use as a manual of comparative STATISTICS complementing the operational installations of USTER® products at the customer site. For technical details on how to ensure proper agreement between the data presented in this document and data established with other USTER® instruments, please refer to the appendix.

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8 Disclaimer

This publication and the information provided therein is for intended use only and subject to change at any time without prior or public notice. Uster Technologies will not assume liability for any direct or indirect damage resulting from unintended use of this publication or the information provided therein. The use of this information for product specifications in commercial contracts is discouraged unless clear reference is made to this publication or parts thereof and clear numerical specifications and tolerances are provided in the contract. The use of this information for arbitration purposes is discouraged unless clear reference is made to this publication or specified parts thereof and clear numerical specifications and tolerances are provided in legally valid contractual documents pertaining to the characteristics of the goods in question. The use of this information for performance guarantees relating to textile plants, textile machines, or parts or accessories thereof is discouraged unless clear reference is made to this publication or parts thereof and clear numerical specifications, tolerances, and restrictive clauses pertaining to other known influences on the specified performance are provided in the guarantee documents.

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9 TestingConditionsandSampleSizes

All tests in relation to the USTER® STATISTICS 2007 were carried out under constant climatic conditions. The temperature was 20 °C, the relative humidity �5%. The following table lists the testing conditions and the sample sizes.

FiberTesting

Parameter Abbreviation Unit Instrument No.of Tests samples within

Micronaire Mic --- USTER® HVI 1 10

Upper Half UHML mm USTER® HVI 1 10 Mean Length UI % 1 10

Bundle tenacity Strength g/tex USTER® HVI 1 10

Color Rd % USTER® HVI 1 10 +b --- 1 10

Trash CNT --- USTER® HVI 1 10 Area % 1 10

Short Fiber Index SFI % USTER® HVI 1 10

Spinning SCI --- USTER® HVI 1 10 Consistency Index

Maturity Index Mat --- USTER®HVI 1 10

Neps Neps/g 1/g USTER® AFIS 1 10 SCN/g 1/g 1 10

Length SFC(n) % USTER® AFIS 1 10 SFC(w) % 1 10 UQL(w) mm 1 10

Maturity Fine mtex USTER® AFIS 1 10 IFC % 1 10 Mat --- 1 10

Trash Trash/g 1/g USTER® AFIS 1 10 Dust/g 1/g 1 10 VFM % 1 10

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YarnTesting

Parameter Abbrevia- Unit Instrument No.of Tests tion samples within

Count CVcb % USTER® 10 1 variations TESTER 4 FA Sensor

Mass CVm % USTER® 10 1 variations CVmb % TESTER 4 10 1 CS Sensor Testing speed: �00 m/min Duration of test: 2.5 min

Hairiness H --- USTER® 10 1 sH --- TESTER 4 10 1 CVHb % OH Sensor 10 1 Testing speed: �00 m/min Duration of test: 2.5 min

Imper- Thin places 1/1000 m USTER® 10 1 fections Thick places 1/1000 m TESTER 4 10 1 Neps 1/1000 m CS Sensor 10 1 Testing speed: �00 m/min Duration of test: 2.5 min

Trash Dust 1/1000 m USTER® 10 1 Trash 1/1000 m TESTER 4 10 1 OI Sensor Testing speed: �00 m/min Duration of test: 2.5 min

Diameter CVd % USTER® 10 1 variation Shape --- TESTER 4 10 1 Density g/cm3 OM Sensor 10 1 Testing speed: �00 m/min Duration of test: 2.5 min

Tensile FH cN USTER® 10 20 properties RH cN/tex TENSORAPID 4 10 20 CVRH % 10 20 εH % 10 20 CVεH % 10 20 WH cNcm 10 20 CVWH % 10 20 Testing speed: 5 m/min

HV tensile FH cN USTER® 10 1000 properties RH cN/tex TENSOJET4 10 1000 CVRH % 10 1000 εH % 10 1000 CVεH % 10 1000 WH cNcm 10 1000 CVWH % 10 1000 FHP=0.1 cN 10 1000 εHP=0.1 % 10 1000 FHP=0.01 cN 10 10,000 εHP=0.01 % 10 10,000 Testing speed: �00 m/min

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TestingofRovings

Parameter Abbreviation Unit Instrument No.of Tests samples within

Count variation CVcb % USTER® 10 1 TESTER 4 Length 10 m FA Sensor

Count variation CVm % USTER® 10 1 TESTER 4 CS Sensor Testing speed: 50 m/min Duration of test: 5 min.

Count variation CVm3m % USTER® 10 1 TESTER 4 CS Sensor Testing speed: 50 m/min Duration of test: 5 min.