Computer Application in Textiles-4 vignesh dhanabalan

Computer applications in textiles –Unit IV

BIAS- Bale Inventory Analysis System is developed by M/S Zellweger Uster. According to this system, mix is formed in a way that bales taken from stock are having minimum variation in quality parameters from bale to bale and from day to day.

Steps

1. 100% testing of bales: All the bales received are tested in HVI. Tested results are exported to BIAS either directly or by floppy disk.

2. Categorization of Bales: All bales tested are numbered and divided into categories. Eg. If categorized according to SCI(spinning Consistency Index) and MIC(Micronaire).

3. Mix Formation: Eg.mix for 20 bales

For SCI – Make 6 categories < 80, 80-90,90-100,100-110,110-120,>120.

For MIC – Make 6 categories < 3.0, 3.0-3.3, 3.4-3.6,3.7-3.9, 4.0- 4.2, >4.2

These categories are decided by analyzing at least 1000 bales for particular season. Since total categories are 6 x 6 = 36, so all the bales tested are stocked into 36 categories. In other words, total of SCI category in any direction will be the same. Now bales issued are subtracted from stock.

4. Print out of Mix Plan: Enter bale number of each bale issued. Average of all quality parameters will be printed. Thus, BIAS helps to reduce variation in quality parameters on a day to day basis for particular mixing.

Fibre information from HVI and yarn information from TensoRapid have been used to improve performance and profitability of the yarn manufacturing process. The EFS and BIAS software enable the user to select bales of raw material which can result in better raw materials utilization, fewer processing problems, improved yarn properties and more profit. Under carefully controlled conditions, the coefficient of determination(R2) for these multiple linear regression statistical methods relating bale and yarn properties can be as good as R2 ~ 0.9. This means, under these best of circumstances, that 90% of the variability in the output yarn can be explained by variability in the input fibre properties. More typical results are R2 ~ 50%, which means that the variations in input fibre properties can only explain 50% of the variations of fibre in the output state, yarn. This means that the 50% which is unexplained is primarily attributable to the variations associated with the processing machinery.

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Engineered Fibre Selection(EFS)

The transition from a process that has been primarily based on intuitive and subjective judgement to a total engineering system of fibre selection could not have been possible without the introduction of powerful bale management and selection software programs. The Engineered Fibre Selection(EFS) system, developed by cotton Incorporated, is the leading program in this regard.

The EFS works in conjuction with the High Volume Instrument(HVI) system to access and manipulate data of cotton fibre attributes. It consists of capable software programs and electronic data exchange system that collectively handle management of cotton bale information, selection of cotton bales for particular spinning systems and planning blending strategies suitable for different levels of yarn and fabric quality.

The powerful features of the EFS system have made it possible for many researchers to develop new algorithms of cotton fibre selection and blending. These algorithms aim at providing practically proven scientific techniques for optimizing the consistency of fibre profile during processing. Indeed the capabilities offered by the EFS system provide ample opportunities for new generations of researchers ti fully engineer the process of cotton fibre utilization from the field to the end product.

The EFS system integrates bale management schemes and fibre selection algorithms to produce cotton mixes designed for different end products. The basic functions of the EFS system in the textile process are shown in the figure. The first function is to handle the logistics of cotton bale information efficiently throughout the flowchart of the cotton market. The second function is to provide different fibre selection and blending strategies suitable for different yarn types and textile end products. The third function is to assist the textile mill in determining optimum levels of yarn quality with respect to the required levels of fibre attributes and the cost of fibres.

The family of software programs used in the system demonstrates the integration feature of the EFS which facilitates the handling of HVI fibre information of different cotton bales from the gin to the textile mill. The EFS consists of three software programs and electronic data interchange mailbox, which facilitates the transfer of HVI data between mills merchants / co-ops and gin direct operations.

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Basic Functions of the EFS System

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Basic Functional Elements of the EFS System

LogisticsOptimization of Yarn

QualityBlending Strategies

HVI Bale Information

Fibre – To- Yarn Conversion System

Bale Management


LINEAR PROGRAMMING PROBLEM (LPP) IN COTTON MIXING

LP is a mathematical model in which we represent and existing situation in terms of linear equations and try to find solutions which will satisfy these equations and at the same time maximize or minimize one of such equations

y = Ax1+Bx2+Cx3

x1, x2, x3 are variables

Interpretation

x1, x2, x3 are Quantity sold for three different products.

A, B, C are Profit per unit

Y is the total profit of the company on the sale of three products.

Usage in Textile Industry:

Determining the optimum blend for mixing

Determining the optimum product mix in production planning

Reduction of inventory of dyes and chemicals

Determining the optimum raw material purchases

Identifying the bottleneck areas in production

LP is quantitative tool for optimal allocation of limited resources amongst competing activities.

Usage of LP / Need for LP

Minimize Total cost and Maximize Quality

Formulation of LP model in cotton mixing

Let

C1, C2, C3 and Cn be the costs of n cottons

P1, P2 , P3 and Pn be the percentage of each cotton to be mixed

L1 , L2 , L3 and Ln be the strength if the cotton fibre

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S1, S2, S3 and Sn be the Maturity coefficient

M1, M2, M3 and Mn be the Micronaire value

Objective function

Min Z = (C1x P1) +( C2x P2) + (C3x P3) +…………+ (Cnx Pn)

Subject to Constraints

L1P1+ L2P2+ L3P3 +………………. + LnPn ≥ Lr

S1P1 + S2P2 + S3P3 +………………. + SnPn ≥ Sr

M1P1+ M2P2 + M3P3+………………+ MnPn ≥ Mr

P1 + P2 + P3+………………………+ Pn = 1

Where P1, P2, P3………..Pn ≥ 0

Example: To manufacture 10ton cotton yarn, the required properties of the raw materials are the following

Length: 31.5mm -34mm

Strength: 20gptex -23gptex

Maturity Coefficient : 80-83%

Micronaire: 3.6 – 3.9

Properties of cotton available and their costs

Cotton Varieties

Properties

1 2 3 Norms

Length(mm) 33 31 30 32

Strength(gptex) 24 20.5 19 21.5

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Maturity Coefficient

83 80.2 79.8 82

Micronaire 3.5 3.85 3.9 3.7

Cost/lb 2.05 1.70 1.66

Objective function

Min Z = 2.05 P1 + 1.70P2 + 1.66P3

Constraints

33P1+ 31P2+ 30P3 ≥ 32

24P1+ 20.5P2+ 19P3 ≥ 21.5

0.83P1+ 0.802P2+ 0.798P3 ≥ 0.82

3.5P1+ 3.85P2+ 3.9P3 ≤ 3.7

P1+ P2+ P3 =1

Minimum Constraints P1, P2, P3 ≥ 0

P1, P2, P3 values are obtained by solving this LP model using simplex method.

Results

Objective function value: Min Z = 1.925

Cotton Percentage to be mixed

1 64.3

2 35.7

3 0

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APPLICATION OF COMPUTERS IN FIBRE TESTING – HVI AND AFIS

Conventional methods of evaluating cotton fibre qualities are time consuming and operator dependent. High speed and reliable testing instruments are the present need of mills to produce quality yarns. The development of high volume instrument (HVI) and Advanced fibre information system (AFIS) are capable of measuring as many of these properties as possible, accurately and at a fast rate too.

High Volume Instrument:

In HVI, various conventional fibre-testing instruments have been automated and integrated into a single compact operating system by using the state of art technology in mechanics, optics and electronics. The different HVI modules are

1. Length and strength module

2. Micronaire module

3. Colour and trash module

General description and procedure of operation

The system is housed in two consoles and the testing sequence is organized from right to left. Two operators are required to work on the HVI identification (ID) number and perform the colour / trash and fineness tests. After the micronaire value gets transferred to the computer, the second operator performs the length and strength tests while the first operator proceeds with testing of the next sample. The number of samples that can be tested per hour is basically decided by the number of replications needed for each sample.

The sample can be tested either in system testing mode or module testing Module testing can be performed for length / strength, Micronaire and colour / trash. Since each test is performed independently from the other modules, only the data for the selected module are collected and reported.

The main difference between system testing and module testing is the way the test results are reported. Module testing produces individual results whereas system testing produces an average of all test results.

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Length / Strength module:

The module comprises a combing device to set the fibres parallel, an optical system for length measurement and a clamping jaw system to determine fibre strength and elongation.

While the preparation of the sample to be tested takes place fully automatically in latest version of fully automatic HVI, with the semiautomatic version, the test sample should be prepared with the help of a fibrosamples, then the sample prepared comb is placed in the comb track. The test cycle begins with automatic brushing of the sample in the fibrocomb. Brushing parallelises the fibres and simultaneously removes the fibres. As soon as the brushing cycle is completed, the comb is automatically pushed from the brushing station to the comb holder. There the test specimen moves through a light beam. When the fibres interrupt the beam, an optical sensor that collects the light transmitted by the fibres and generates voltage proportional to the mass of the fibres interrupt the beam at any instant of time, from which the fibrogram is constructed by the microprocessor and the software. Various span lengths can be obtained simultaneously, thought normally the software option is limited to print /display only the 2.5% S.L, 50% S.L and the Uniformity ratio. Other parameters like upper half mean length(UHML), Uniformity Index(UI) and short fibre index(SFI)can also be obtained along with the fibrogram. SFI is the percentage of fibres having length < 12.75 mm.

The Strength measurement is also carried out during the same operating cycle. After the test specimen has been moved through the optical sensor, the board is positioned automatically at the break point over a set of clamping jaws with 0.125” spacing between the set of jaws. The jaw faces are lined by polyurethane and the sample is pneumatically clamped between the jaws, with sufficiently high pressure to prevent fibre slippage. The breaker, which is controlled by a stepmotor, pulls the fibres at a constant rate of extension at the same time recording the ouput of a free transducer as a function of the distance through which the rear jaws have moved. When the limit of travel is reached, the sample beard would have broken. The force required to break the beard is calculated and converted into g/tex with the help of software. The breaking elongation calculated from the average distance between the set of jaws before the sample is broken is also provided by the instrument. Results can be printed or stored. Stress-strain curve can be obtained as per the operator’s requirement.

Principle of measurement procedure for micaronaire:

The micronaire module of HVI works on the same principle as shefiled micronaire. It consists of an electronic balance, a porosity chamber and a microprocessor that receives and digitizes the relay voltage from the differential pressure transducer. Place well opened sample on the balance. The sample must weigh between 8.5 and 11.5 grams for the micronaire test to be performed. Add

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or remove fibre until the sample weight is within the acceptable limits. As soon as the sample weight is within range, the airflow in the micronaire chamber is initiated. Now insert the weighed sample into the micronaire chamber and close the lid. When the compressed air is allowed to flow through the sample inside the chamber, differential pressure develops across the chamber. From the measured values of mass and pressure, microprocessor calculates the specific surface area, which is converted in to “Micronaire value” by the data processor. After the measurement the sample gets automically ejected from the chamber.

Principle and measurement procedure for colur and trash:

For measuring the colour, enough quantity of sample to block the light is placed on a glass window provided in the colour / trash module of the instrument. Now press the button located in the right side of the glass window to begin the measurement process. The colour hand is activated and will apply 20 pounds of pressure to the sample. The calorimeter uses two lamps to illuminate the sample at 45 degree angle, during testing the reflected ight passes through filters and photodiodes to measure the two colour components of cotton (brightness and yellowness). Brightness is expressed as a percent reflectance and yellowness is expressed in hunter’s scale.

Trashmeter:

While measuring colour it measures the trash content. The trash meter consists of a video camera, video monitor and a microprocessor. The video camera scans the illuminated sample and the microprocessor digitizes trash area, trash count which is the number of trash particles of diameter 0.01” or more.

Advanced fiber information system

AFIS provides single fiber information and is distinguished from earlier and existing methods by providing distributions of basic fiber properties.

The different AFIS measurement modules are

1. The L and M module

2. Trash Module

3. Nep Module

The above module can be used for different purposes depending on the type of information needed.

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AFIS method of measurement:

A fibre sample is introduced into the system and is processed through a fiber individualizer, which aero mechanically separates the sample into three components consisting of cleaned fiber, microdust and trash. Each of these components is transported in a separate pneumatic path and analyzed electro-optically. The data processing and reporting are handled by a PC.

Basic function of fiber individualizer:

The fiber individualizer uses unique cleaning and separating techniques to present the fibers pneumatically to the electro-optical sensor. The fibers are opened and cleaned using specially designed pinned and perforated cylinders, which are similar to open-end spinning beaters, and stationary carding flats. Airflow into the perforations of the cylinder allows for through engagement and efficient dust and trash removal. The separated components are transported along three different pneumatic paths.

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Central Processing Unit

Data

Sample

Fibre Individualizer

Electro – optical sensor


Electro – optical sensors:

The Electro – optical sensors consists of three basic elements

1. Tapered entrance and exit nozzles

2. Beam forming and collection optics

3. The detection circuitry

Single fibre

Vo

V1

Source Detector

Individualized fibers are transported pneumatically from the fiber individualizer by an air stream. They enter the Electro optical sensor through an accelerating nozzle, which straightens, separates and aligns the fibers in proper orientation to the source detection. The fibers penetrate a collimated beam of light and scatter and block that light in proportion to their diameter and in direct relation to their time of flight thorough the sampling volume.

Generally, rectangular waveforms are produced by the light scattered by individual fibres. Nep signals are much greater in magnitude and duration and generate a characteristic nep “ spike”. Trash particulars produce smaller spiked waveforms, which are distinguishable from neps in magnitude and duration.

From these waveforms, which are microseconds in duration, the pertinent data are acquired, analyzed and stored in the host computer. Distributions based on size, length or diameter can be generated.

The L and M module:

This module gives various length parameters, their distributions and short fiber percentages both by number and weight. In addition, this module gives fineness in millitex, maturity expressed as Immature Fiber Content (IFC) and Maturity Ratio (MR).

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The SFC from AFIS agrees to a certain extent with SFC from comb sorter irrespective of fibre length.

Fibre Maturity

Many tests in spinning mills have clearly shown that 70% of yarn related dye problems have their origin in the fiber itself. Hairiness, yarn count and yarn twist variations contribute only 10% each to the problems. To avoid fabric bare maturity variation should be controlled. Therefore it is essential to measure maturity apart from micronaire value. Micronaire is not a valid indicator for maturity for all cotton varieties.

Traditional Airflow instruments measures only average maturity and fineness of a cotton sample. With the AFIS length and maturity module it is possible to determine not only length, but also the maturity of a single fiber and the variation thereof. Further it measures the immature fiber content (IFC) and the fibre fineness in millitex.

IFC indicates the percentage of fibers with less than 0.25 circularity. The lower the IFC%, the better the dyeability.

Trash Module:

It gives the size distribution of trash particles along with trash content. The gravimetric trash data provided by trash separators will be more reliable in commercial transactions as the estimation itself is based on a large sample size of 200 gm.

Nep Module:

The neps are classfified into two categories: fiber neps and seed coat neps. The main feature of this module is that it distinguishes fiber neps from seed coat neps. The nep information obtained from this module will be more useful for breeding progrmmme and process control in textile mills.

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UT-4 (USTER TESTER-4)

Capacitive method of the uster testerThis method id dependent on the fact that there is a linear relationship between the fiber mass in the measuring slot and the output signal of the measuring unit. After measurement, a calculator determines the mean mass over the complete measuring time.This method of measurement can be considered as being intended primarily for the following applications:

Trials with respect to count variations in test lengths which do not correspond to the usual standards.

Trials with respect to count variations in slivers and rovings, and with respect to test lengths which cannot be handled with the normal gravimetric-type instruments due to the volume of material.

Determination of the count variations within and between packages, this is to be carried out without the use of an elaborate gravimetric system.

Influence of the measuring field length

Measuring principleThe evenness tester utilizes the principle of electrical capacitance to measure the mass variations of sliver rovings and yarns. In capacitance measuring, a non-conducting material passes between two parallel capacitor plates. Mass variations of the test material will change the electrical signal from the sensor. The electrical signals are amplified, analyzed and recorded. In this manner, mass variations of the tested material are very quickly and accurately measured.

Measuring field lengthIn the testing of fiber assemblies with the evenness testing installation, the test material is measured in various measuring slots of the measuring condenser according to the count of the material and the process stage from which it has been taken. The length of the shortest mass variations which can be recorded on the diagram and which will correctly correspond to the amplitude is mainly dependent on the length of the measuring field. This is formed by the electrode plates of the measuring condenser.The shorter the measuring field length, the shorter will be the wave-length of mass variations which can be recorded. The measuring field length, however, cannot be made as small as one would wish due to design and stability reasons.

The following are the measuring field lengths for UT3 and UT4Type of material Measuring length

SliverRovingYarn

20mm12mm8mm

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This minimum length of the measuring field results in the fact that the mean value of mass which lies between the measuring electrodes is determined and indicated. From this, it can be concluded that variations which are shorter than the measuring field length cannot be indicated according to their full amount but appear as a reduced value according to a certain relationship.

Determination of the diameter, density and roundness of yarns

The two –dimensional optical diameter measurements by the USTER TESTER 4 optical OM-sensor makes it possible to measure the variations of the yarn structure which are not visible in the capacitive mass measurements. For example, hard or soft-twisted pieces of yarn may show the same mass but vary in the yarn diameter. This type of fault is likely to have an influence on the appearance of yarns and fabrics.

Measuring principle of the OM sensor

The optical multifunctional(OM) sensor measures the diameter from the sides, by means of a crosswise placement of optical transmitters, mirrors and receivers on each side.

The measurement of the yarn body is based on combined digital-analog sensor technology, which contains a high-resolution line scan camera and an integrated analog sensor which operate together. The infrared transmitter of the OM sensor provides a nearly parallel light beam. A sharp image of the yarn on each optical receiver is created by integrated optics. The signal processing circuits evaluate the precise yarn body diameter at each scan interval. The yarn’s protruding fibres do not influence this measurement.

The crosswise arrangement of the sensors at angels of 0° and 90° provides information on the average two-dimensional(2D) yarn diameter and the shape of the yarn. The output data of the OM sensor module s further processed by the USTER TESTER 4’s computer to generate the numeric and graphical data.

Being optical, the OM sensor is also a valuable tool for measuring the evenness of special yarns containing electrically conductive materials, such as steel, carbon fibres etc. It must also be added that the OM sensor is not sensitive to the colour and the brightness of the object to be tested.

Characteristics of the sensor

The measurement of the optoelectronic OM sensor is absolute. As a result of the two-dimensional arrangement of the sensors, the system evaluates the mean core diameter of the yarn and therefore provides a more accurate description of the yarn cross-section. Disturbing fibre

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assemblies which protrude from the yarn body and very short-wave variations are fully considered in the evaluation.

The OM sensor provides reproducible measurement values, irrespective of the yarn speed. Independently operating cleaning and control mechanisms counteract any influences of dirt and fly. With a measuring field length of 0.3mm, even very short-wave variations are considered in the quality assessment. These variations in particular are often considered optically disturbing.

Evaluation possibilities

With the OM sensor the following additional quality information about the tested yarn body can be obtained

Determination of the mean yarn diameter in millimeters over the entire measuring length

Recording of the absolute diameter variations in the form of a diagram

Display of a spectrogram for the detection of periodic diameter variations

Display of a histogram for determining the distribution of the diameter variation

Display of the variance-length curve

Determination of the shape of the yarn over the entire measuring length

Calculation of the density from the nominal count and the mean cross-section area.

The statistical values such as the coefficient of variation and the standard deviation are related to specific reference lengths.

UT-5 (USTER TESTER-5)

Incredible testing speed of 800 m/min

Mass and yarn count variations, thin places, thick places and neps can influence the quality and the sales price of the yarn enormously. With the USTER® TESTER 5-S800, these quality parameters can be determined with an incredible test speed of 800 m/min. The optoelectronic sensors of the USTER® TESTER 5-S800 give additional quality-relevant information. One innovation, exclusive to USTER®, is having foreign fiber measurements integrated into the USTER® TESTER 5-S800. With the new feature Fancy Yarn Profile the S800 offers all benefits for a precise quality control. Only USTER®’s unique sensor technology guarantees a hitherto

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unobtainable degree of precision – and the measuring accuracy which is the benchmark for the textile industry.

OASYS

In 1997 Zweigle Textilprofmaschinen introduced the new generation of integrated optical systems for the visual assessment of yarns in woven and knitted fabrics. Just as a small seedling that starts slowly into a large tree, sales of the system have developed in a similar manner. Today optical testing is used in many areas of yarn testing and provides a great deal of flexibility in future quality control. OASYS gives a comprehensive report on the quality if yarns in just few minutes.

OASYS works virtually and sees just like the human eye. The OASYS measuring system operates on the principle of absolute optical measurement using infrared light. It sees yarn defects just like the human eye: thick and thin places, irrespective of whether these areas contain more or fewer fibres than the normal yarn. The capacitance evenness testers used today fail to provide meaningful information on the quality of the end-product.

The structure of a yarn is subject to variations of a periodic or random character. The measuring system compares the yarn diameter with the constant reference mean and records variations in length and diameter.

The yarn testing module use an infrared light sensor operating with a precision of 1 / 100 mm over a measuring field length of 2mm and at a sampling interval also of 2 mm. The speed of measurement may be selected on a graduated scale between 100 and 400 m / min.

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The sensor operates in any environment with equal precision, both in the laboratory and under production conditions.

It is unaffected by ageing of the light source, extraneous light, contamination, temperature and humidity.

It is unaffected by such yarn characteristics as colour, conductivity or luster.

There is no need for time consuming conditioning of specimens.

Its principle of absolute measurement does not require repeated calibration and re-entering of parameters

The essential functions of the OASYS system lies in its ability to simulate yarn irregularities on boards and woven and knitted fabrics using the previously measured yarn data.

The classification matrix comes as standard equipment with the system. This matrix classifies all defects in respect to their length and their variation in diameter. This is very useful for classifying and defining major yarn defects. Settings can be modified before each test is carried out.

The IPI – Values are indicated by the colour zones and record the neps, thick and thin places per km.

A Reference Diameter is established in the first 100 m. This reference value is used as a bsis for all statistical comparisons in the classification matrix and IPI-values.

On the right adjacent to the matrix, the statistical means for the yarn are shown: neps, thick places, thin places, OCV index and reference value.

The Histogram shows the diameter distribution in the yarn.

The Spectrogram shows the periodic defects in the yarn. A tower indicates that this defect occurs with relatively strict periodicity. This diagram shows the frequency distribution of the wavelengths that occur.

The CVL- Curve represents the variation length coefficient in a diagram

The Optical CV%-Index (OCV%) shows the amount of “true” irregularity of a yarn and can not be compared to the capacity method.

Amongst the reasons for this incompatibility are

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Different principles of measurement (optical – determination of diameter, capacitive – determination of mass variations)

Different test zone lengths(integration stages) , capacity method 8mm, optical(OASYS) method 2 mm.

Different methods of correlation ( Zweigle OASYS absolute, capacity relative)

Statistical comparisons allow you to undertake various tasks such as diagram overlays for displaying several tests in a single diagram.

Weaving Module – Realistic woven fabric simulation by OASYS – Weaving Module

The OASYS – Weaving Module incorporates eight popular weave interlacing programs. In the case that more flexibility is required, Zweigle has developed an internationally patented weave editor that is incorporated in the OASYS – Weaving Module Plus.

OASYS – Pattern Editor

Quick selection of your weave Interlacing using the OASYS – Pattern Editor

Clicking once on the “Weave”symbol brings up the Weave Editor. The window shows empty point paper ready for you to work on. Various menus are available for the purpose of creating new weaving patterns.

The Edit menu allows you to:

Remove, copy and insert blocks.

Insert weaves from a database.

Invert the weave or block

The Warp/ Weft menu allows you to:

Insert warp /weft lines

Delete warp/weft lines

Transpose within warp and weft lines

Transpose an interlacing point

OASYS – Stone wash module

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Our new OASYS stone wash module keeps you abreast of fashion, providing denim simulations with localized or all-over washed-out effects.

As the inserted parameters show, “stone-wash” effects can be simulated either over the full area or in localized areas of pre-selected diameter. The lighter shading and the intensity of the shading are continuously variable.

OASYS –Knitting Module

OASYS –Knitting Module for highly realistic simulation of Single Jersey fabrics.

Additional knit constructions are available with the OASYS – Knitting Module Plus:

These are Double Jersey, Piquet and Interlock.

Define the geometry of the knit fabric by entering via the settings tab.

Select the yarns used in the knit via the yarn sequence tab

OASYS – Yarn Board Module

The traditional way to display yarn evenness is on a board or panel. The yarn is wrapped on this surface in the shape of either a rectangle, single trapezoids or four trapezoids, which when placed together, have approximately the dimensions of a standard yarn board.

Define the panel geometry by entering the following via the settings tab:

Wind –on density

Panel width

Panel height

Scale

Shape of the panel.

Fancy Yarn Module

Since ten years the OASYS –System has been the tool of choice or slub yarn spinners and fabric manufactures that use slub yarns. The optical measuring system has proven to be the system of choice for the evaluation of slub yarns. The industry philosophy is that one must measure what is later actually seen in the fabric as an effect. This OASYS in fact does due to the optical measurement as opposed to capacitance systems which only concentrate on the yarn volume.

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In a effort to support the industry, Zweigle has developed a number of tools for measuring slub yarns. These tools allow the industry to document slub yarn parameters statistically and graphically. Additionally the OASYS system supports in the design of new slub yarns and fabrics by creating yarns in a virtual environment. These yarns can then be used to make fabric simulations in OASYS.

Statistical slub evaluation

The classification Matrix is a tool to evaluate slubs numerically

User defined slub length settings

User defined slub thickness settings

Numerical classification of slubs

Popular tool to guarantee reproducibility of slubs.

The yarn analysation portion of the program displays the complete measured yarn profile. The operator can easily take measurements on any portion of the yarn up to 10,000 m using the slides. It is possible to zoom onto specific portions of a slub and to adjust the zoom or the magnification of the amount of yarn which is under investigation. Using the locator function consisting of two red and blue lies detailed measurements of the yarn can be gained in record time. This allows for the determination of slub length, slub thickness, pause length and slubing patterns.

Also available in OASYS are graphs which can how slub information on spectrograms and histograms. Histograms show the slub thickness and their distribution while the spectrogram is a tool to determine the periodicity of the slubs. Used together this information is helpful in the development of new slub yarns and the archiving of different slub yarns and their parameters.

Copy slub yarns

It is now possible to copy slub yarns in a few minutes time. After measuring a yarn all revelent parameters can be gotten from the before mentioned tools and then easily entered into various slubbing systems.

Creating Slub yarns

OASYS allows for the creation of slub yarns in a virtual environment. Design and sampling costs along with quick reaction times have long been a focal point. OASYS serves as an excellent tool to reduce costs associated with sampling. Aside from actual measured yarns it is possible to

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create slub yarn in a virtual environment. Using the yarn generation program all parameters of a slub yarn can be created. These virtual yarns can then be placed in various simulation programs to see the appearance of slubs in a new fabric design before the yarn has actually been spun.

CYROS

CYROS is the revolutionary new CAD/CAM system which combines the measurement of actual yarn with high resolution computer simulation of textile to accurately depict the yarn’s visual effects in final fabric form. CYROS is the very first system to advance this level of computer visualization technology to the yarn manufacturing segment of the textile industry. CYROS is a quality control, research and development tool for fabric, yarn and fibre manufacturers.

CYROS operates with yarn evenness measurement devices. It directly converts precise yarn diameter values to a 3D –yarn model for process into a fabric simulation – much like today’s high end CAD/CAM systems. The major difference is that CYROS deals with the actual thick and thin properties of real yarn rather than the virtual yarn which is ‘perfect’ in construction.

CYROS can process multiple yarns and various feed arrangements for both knit and woven fabric simulations. Production parameters can be set for the knit and weave process as if the yarn was being processed as actual fabric production. The results are realistic true to scale fabric images on the basis of actual yarn. CYROS also produces the yarn simulation wrapped in the traditional yarn board format.

CYROS is indeed the new generation of textile development tools.

Commercial systems such as CYROS produce fabric images based on a very limited amount of off-line yarn data, seldom reflecting the effects if medium to long term yarn variations that can only be obtained from a large amount of production data. Because of the enormity of data and lack of efficient data reduction, compression and regeneration technologies, no commercial system is capable of retaining even a fraction of the data being generated. Consequently, it is only after the actual weaving and knitting that certain fabric defects are detected and corrected.

Premier Tester 7000

The new generation evenness tester from Premier is accepted worldwide as a necessity for every spinner. With a continuous growing library of machines for spectrogram analysis, PT 7000 has become a universal quality control tool in identifying and correcting faults in machinery and the process. PT 7000 not only comes with a 3 dimensional fabric simulation system Premier – CYROS offering precise assessment to yarn quality in terms of fabric appearance.

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PREMIER CYROS Yarn and Fabric Simulation System

When the yarn is tested in the PT7000 evenness tester, it is also optically scanned by an optical sensor. The sensor determines the physical yarn dimensions and transposes into three dimensional yarn models. The 3D yarn models are used in turn to simulate a 3D appearance of the fabric structures as may be programmed by the user. The simulation software works on an independent PC.

The benefits of the system include detection of defects before actual weaving or knitting resulting in considerable saving of material and time. It is also possible for the user to optimize the process based on the simulation.

Fabric Structures

Weave : Plain, Twill, Oxford

Knit : Single Jersey and Interlock

Yarn Appearance Board: Rectangular and Tapered

Optical Yarn Statistics : CV% Imperfections Classes

Yarn count, variety and density in the warp and weft directions are selectable.

Additional options

Denim

Special module working together with a stonewash processor with facility of entering control parameters like colour bleeding, wash strength, yarn colour, “stone” size. etc.,

Amsler Slub effect

Direct import facility of Amsler ETX for simulation by CYROS

Colour database

USTER FABRISCAN ON- LOOM

Detecting fabric defects when they occur, removing the cause at an early stage, therefore preventing defective products. USTER has the solution for absolute inspection. The new USTER FABRISCAN ON-LOOM integrated fabric inspection system not only give you automatic quality control directly at the loom, it can replace the customary cloth inspection.

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Full-area inspection

Comprehensive result

No system can inspect closer than touching the fabric surface during inspection. No-one can cover more than the entire area of the fabric. The revolutionary USTER FABRISCAN ON- LOOM inspects every single millimeter of the fabric and checks it for defects. The scanner sensors are in direct contact with the fabric and deliver high-quality images for reliable detection of defects.

The USTER FABRISCAN ON- LOOM prevents faulty production since, through immediate detection of defects, it enables immediate correction. Quality is markedly improved, reliability of inspection is assured.

Integrated vibration control

Best image quality

USTER FABRISCAN ON- LOOM has a patented system for compensating the vibrations of the loom. Having the sensors in direct contact with the fabric improves the image quality. This means that recordings are never shaky or blurred like those from a camera system, but are precise images of the fabric structure and provide exact identification of the defect.

Prevention of second grade fabric

Increase in profit

Random quality checks are not as certain as comprehensive detection. Often, running, swarm and periodic defects are not discovered until it comes to cloth inspection – hours or even days later – too late to prevent the production of second grade fabric.

USTER FABRISCAN ON- LOOM detects and reports defects immediately as they occur. Early detection enables early intervention – independently of human perception. Automatic inspection is reliable and consistent, and assessment is based on objective criteria. The productivity of the first grade fabric can be increased.

Replaces the cloth inspection

USTER FABRISCAN ON- LOOM is a perfect warning system when accompanied by the On-Loom Expert System, it replaces the cloth inspection. Quality reports, image and graphic maps of defect distribution are produced online.

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Reports for monitoring productivity and statistical analyses of efficiency are also available. Data and images can be edited and re-archived – and are useful for verifying any customer complaints that may arise.

Programming

USTER FABRISCAN ON- LOOM can be individually programmed. Depending on the type or quality of a fabric, other criteria may be important for quality control. With the USTER FABRISCAN ON- LOOM one can individually vary the sensitivity of inspection according to category of defect, to suit one’s requirements. These are then stored and can be called up at any time.

Quick and simple operation

Optimal positioning

Operation is quick and easy – new articles can be read in with no great effort. Data on type and position of defect is output on an easy to understand display.

USTER FABRISCAN ON- LOOM is mounted on the front of the loom. Positioning it there saves space and keeps it out of the weaver’s way. This is also the ideal place to successfully compensate for the vibrations of the loom.

Wide range of applications

USTER FABRISCAN ON- LOOM is delivered and assembled in the set-up one need- individually tailored to the width and model of a loom. The system is also universal in respect of the fabrics produced. The new fabric inspection system is suitable not only for plain cloth, but also for more complex weaves and coloured patterns, dyed yarn, technical fabrics and denim.

Integrated process control

USTER FABRISCAN ON- LOOM provides certainly and enables improvements in the weaving process. Together with the proven fabric inspection system USTER FABRISCAN an absolute control over the produced quality from weaving to finishing is possible. The consistency and objectivity of the results are guaranteed and one can save time and money because the cloth inspection is no longer used.

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DIGITAL IMAGE PROCESSING

An image may be defined as a 2D function f(x,y) where x and y are spatial(plane) coordinates and the amplitude of f at any pair of coordinates (x,y) is called the intensity or gray level of the image at that point. When x, y and the intensity values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. A digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are called picture elements, image elements, pels and pixels. Pixel is the term used most widely to denote the elements of a digital image.

Fundamental steps in Digital Image Processing

Two methods are followed in Digital Image Processing. In the first method, the inputs and outputs are images and in the second method, the inputs are images and outputs are attributes extracted from those images.

Fundamental steps involved are

1. Image Acquisition : This is the first process which involves preprocessing such as scaling.

2. Image enhancement : This is the process of manipulating an image so that the result is more suitable than the original for a specific application.

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Outputs of these processes generally are Image attributes

Fundamental Steps in Digital Image Processing

3. Image restoration: This is an areas that also deals with improving the appearance of an image. Unlike enhancement, which is subjective, image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation.

4. Colour image processing: This is the area that has been gaining importance because of the significant increase in the use of digital images over the Internet. This covers a

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Knowledge base

Wavelets and Multi resolution Processing

CompressionMorphological Processing

Representation and description

Object Recognition

Colour Image Processing

Segmentation

Image filtering and enhancement

Image Restoration

Image Acquisition

Outputs of these processes generally are Images

Problem domain


number of fundamental concepts in colour modes and basic colour processing in a digital domain.

5. Wavelets are the foundation for representing images in various degrees of resolution. Images are subdivided successfully into smaller regions.

6. Compression techniques for reducing the storage required to save an image or the bandwidth required to transmit it.

7. Morphological Processings deals with tools for extracting image components that are useful in the representation and description of shape. The material in this chapter begins a transition from processes that output images to processes that output image attributes.

8. Segmentation Procedures partition an image into its constituent parts or objects.

9. Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the boundary of a region.

10. Recognition is the process that assigns a label to an object based on its descriptors.

Components of an Image Processing System

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Computer

Specialized Image Processing Hardware

Image sensors

Hard Copy

Image Displays

Image Processing software

Mass Storage

Problem domain

Network


The functions of various components of Image processing are the following

1. Image sensing: Two elements are required to acquire digital images. The first is physical device that is sensitive to the energy radiated by the object we wish to image. The second called a digitizer is a device for converting the output of the physical sensing device into digital form. For instance, in a digital video camera, the sensors produce an electrical output proportional to light intensity. The digitizer converts these outputs to digital data.

2. Specialized image processing hardware usually consists of the digitizer just mentioned plus hardware that performs other primitive operations such as ALU

3. The computer in an Image Processing is a general purpose computer and can range from a PC to a super computer.

4. Software for image processing consists of specialized modules that perform specific tasks.

5. Mass storage capability is a must in Image Processing

6. Image Displays may be a colour display like a TV monitor.

7. Hard Copy devices for recording images include laser printer, film cameras, heat-sensitive devices, inkjet units and digital units such as optical and CD ROMs.

8. Networking is almost a default function in any computer system in use today. Because of the large amount of data inherent in image processing applications the key consideration in image transmission is bandwidth.

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ARTIFICIAL NEURAL NETWORK

An artificial neural network is an information – processing system that has certain performance, characteristics in common with biological neural network, have been developed as generalization of mathematical model of human cognition as neural biology, based on the assumptions that

1. Information processing occurs at many simple elements called neuron.2. Signals are passed between neuron over connection link3. Each connection link has as associated weight, which in a typical neural net, multiplies

the signal transmitted4. Each neuron applies an activation function (usually non linear) to its net input (sum of

weighted input signals) to determine x1 output signal.A neural network is characterized by

1. Its pattern of connections between the neurons(called its architecture)2. Its method of determining the weights on the connections(called its training or learning

algorithm) and3. Its activation function

A neural network consist of large number of simple processing elements called neurons, units cells or nods. Each neuron is connected to other neurons by means of directed communication links, each with an associated weight. The weight represents information being used by the network to solve a problem. Neural networks can be applied to a wide Varity of problems, such as storing and recalling data as patterns, classifying patterns, performing general mappings from input patterns to output patterns, grouping similar patterns or finding solutions to constrained optimization problems.

Each neuron has internal state, called its activation as activity level, which is a function of the inputs it has received. Typically, a neuron sends its activation as a signal to several others neurons. It is important to note that a neurons. It is important to note that a neuron can send only one signal at a time, although that signal is broad cast to several other neurons.

For example, consider a neuron Y that receive inputs from neurons X 1, X2 and X3. The activations (output signals) of these neurons are x1, x2, and x3 respectively. The weight on the connections from X 1, X2 and X3 to neuron Y as w1, w2 and w3 respectively. The net input y in to neuron Y is the sum of the weighted signal from neurons X1, X2 and X3

(ie)Yin = w1x1 + w2x2 + w3x3

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The activation of neuron Y is given by some function of its net input,Y = f (y - in) e.g. . The logistic sigmoid function

f(x) = 1 . 1 + e (-x)

As any of a number of other activation functions.

Now suppose further that neuron Y is connected to neurons Z1 and Z2 with weights V1 and V2 respectively. Neuron Y sends signal y to each of these units. However in general, the values received by neuron Z1 and Z2 will be different, because each signals is scaled by the appropriate weight, V1 and V2. In a typical network, the activations Z1 and Z2 would depend on inputs from several or even many neurons

V2

Although the neural network is very simple, the presents of a hidden unit, together with a nonlinear activation function, gives it’s the ability to solve many more problems that can be solved by a network with only input and output units. On the other hand, it is more difficult to train a network with hidden units.

Several key features of the processing elements of artificial neural networks are suggested by the properties of biological neurons that

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X1

X2

X3

Y

W1

W2

W3

X1

X2

X3

Y

W1

W2

W3

Z1

Z2

V1

Input Units Hidden Units Output Units


1. The processing element receives many signals.2. Signals may be modified by a weight at the receiving synapse.3. The processing element sums the weighted inputs4. Under appropriate circumstances, the neuron transmits s single output.5. The output from a particular neuron may go to many other neurons (the axon branches).

Other features of artificial neural networks that are suggested by biological neurons6. Information processing is local (although other means of transmission, such as the

activation of hormones, may suggest means of overall process control)7. Memory is distributed

a. Long term memory resides in the neurons synapses or weights.b. Short term memory corresponds to the signals sent by the neurons.

8. A synapse’s strength may be modified by experience.9. Neurotransmitters for synapses may be excitably or inhibitory.

Difference from conventional computingNeural networks perform computation in a very different way than conventional computers, where single central processing unit sequentially dictates every piece of the action. Neural networks are built from a large number of very simple processing elements that individually deal with pieces of a big problem.

ANNS in textile technology

1. Predicting copolymer composition

The free radical copolymerization of acrylamide with a quaternary ammonium cationic comonomer, diethylaminoethyl acrylate has been investigated in inversion emulsion. The copolymer composition was determined from residual monomer concentrations using an hplc method. ANN has been to predict the copolymer composition.

2. Electrically conductive polymers as PH sensors

The introduced integrated ANN has shown much better reproducibility in detecting PH of different acidic and basic media.

3. Animal fibre classification

A nonlinear ANN is compared with linear ANN and a hybrid ANN nas been developed to differentiate between two popular animal fibres- merino and mohair.

4. Prediction of yarn properties using natural computation

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V1


The development of high performance yarns with properties that closely match the requirements of customers require a detailed insight into the influences of process variations and structure arrangement on yarn properties. The relationship between process settings, physical molecular structure and mechanical properties of PET yarns were determined by ANN.

5. Applying ANN to pattern recognition fabric defects

The efficiency and accuracy of a method of detecting fabric defects that have been classified into different categories by a neural network are evaluated. Four kinds of fabric defects most likely to be found during weaving were learned by the network. The method used for processing image feature extraction is a co-occurrence based method, by which six parameters are obtained. The results show that fabric defects inspected by means of image recognition in accordance with the artificial neural network agree approximateky with initial expectations.

6. Computer match prediction for fluorescent dyes by neural networks

Fluorescent dyes present difficulties for match prediction due to their variable excitation and emission characteristics, which depend on a variety of factors. The production of a database with four acid dyes is described, along with the large number of mixture dyeing that were carried out. The data were used to construct a network connecting reflectance values with concentrations in formulations. The results showed that ANN approach is viable and accurate.

7. Modelling of tensile properties of needle-punched nonwovens using ANN

The modeling of tensile properties of needle-punched nonwoven fabrics produced from the blends of jute and polypropylene fibres with varying fabric weight, needling density and blend ratio has been done using ANN.

8. Dynamic mechanical properties of short fibre composites

Dynamic mechanical properties of short fibre reinforced composites were investigated in a temperature range from-150 to 150°C. A series of Polytetrafluroethylene (PTFE) based composites blended with different contents of polyetherether ketone (PEEK) and reinforced with various amounts of short carbon fibres was considered in this paper. Dynamic mechanical thermo analysis was employed using a three point bending configuration. The influence of different characteristics of different characteristics of PTFE and PEEK at various temperature was also considered.

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Based on measured results ANN approach has been introduced for further prediction purposes. The analysis shows that the number of training dataset play, a key role to the ANN prediction quality.

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Computer Application in Textiles-4 vignesh dhanabalan

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