This document describes a study that developed a regression model showing the relationship between mechanical yarn stretch percentage in the sizing process and warp yarn breakage in looms. The study was conducted at a textile factory in Bahir Dar, Ethiopia using 20's cotton yarn. Yarn samples were treated with different levels of mechanical stretch percentage during sizing while keeping other sizing parameters constant. The treated yarn beams were loaded onto looms with identical settings. Analysis of variance showed that mechanical stretch percentage significantly affected warp breakage. A regression model was developed that had a correlation coefficient of 84.4%, indicating a strong relationship between mechanical stretch and warp breakage.
Jute reinforced PLA bio-composite for production of ceiling fan bladesNeway Seboka
This document discusses the use of jute fabric reinforced poly lactic acid (PLA) bio-composites for producing ceiling fan blades. Jute fabrics were treated through various chemical processes including alkali treatment, acetylation, silane treatment, and maleic anhydride treatment to improve their tensile properties and adhesion to the PLA matrix. Composites were produced with different numbers of fabric layers and tested for their mechanical properties. Test results showed that alkali+silane treated jute fabric composites exhibited the highest tensile strength and modulus. The produced bio-composite blades were found to reduce the weight and power consumption of ceiling fans compared to traditional aluminum blades.
High Volume Instrument in QC Department of Textile IndustryMohiuddin Chowdhury
The testing of fibers was always of importance to the spinner. It is done by the HVI machine. High volume instrument systems are based on the fiber bundle strength testing, ie, many fibers are checked at the same time and their average values determined. Traditional testing using micronaire, pressley, stelometre, and fibrous graph are designed to determine average value for a large number of fibers, the so called fibre bundle tests. In HVI, the bundle testing method is automated. Here, the time for testing is less and so the number of samples that could be processed is increased, quite considerably. The influence of operator is reduced.
Handle of cotton: wool knitted khadi fabriciosrjce
Hand of cotton: woolhand knitted fabrics has been reported in this study. Indian crossbred wool
(Rambouillet and Chokla) was blended with cotton (Mech I) in three different ratios (10-90%, 20-80% and 30-
70%) and yarns were prepared on hand spinning system.Knitted fabric samples were constructed on 10-12
gauge, flat bed hand knitting machine. Fabric handle was objectively assessed by SiroFAST.
The document discusses the fibrograph machine, which analyzes cotton fiber length and uniformity. It scans fiber samples using photoelectric cells and produces a length-frequency curve called a fibrograph. Key information included:
- The fibrograph prepares fiber samples using a comb to pick fibers randomly from a cylinder sample.
- It optically scans the fibers from base to tip to analyze fiber length parameters like mean length and uniformity index.
- These measurements provide objective and reproducible analysis of fiber length and uniformity compared to other testing methods.
- The document outlines the machine components, testing process, data analysis, and limitations of the fibrograph method.
This document provides details on testing the length and uniformity of cotton fibers using a Fibrograph instrument. It describes the apparatus, sampling and specimen preparation methods, testing procedures, calculations, and reporting of results. Key details include:
- The Fibrograph scans fiber samples to create a fibrogram curve from which fiber length and uniformity measurements are derived.
- Samples are prepared by combing fibers randomly onto combs and inserting them into the Fibrograph.
- The instrument measures the span lengths at specific percentage points along the fibrogram curve, from which average lengths and a uniformity ratio are calculated.
The document discusses various types of textile testing instruments used to test quality at different stages of textile production. It introduces instruments like the GSM cutter, Martindale abrasion and pilling tester, air permeability tester, lea strength tester, Uster evenness tester, wrinkle recovery tester, crease recovery tester, yarn count tester, yarn twist tester, standards tumble dryer, lab conditioner, fabric thickness gauge, yarn strength tester, and tearing strength tester. It provides details on how each instrument works and the procedures to test quality parameters like weight, abrasion resistance, air permeability, strength, evenness, wrinkle recovery, thickness, and tearing strength.
This slideshow briefs about the need for testing textiles with an example and images that can be supportive to understand. This will be the first of the presentation that leads to fiber, yarn and fabric tests as separate presentations
Jute reinforced PLA bio-composite for production of ceiling fan bladesNeway Seboka
This document discusses the use of jute fabric reinforced poly lactic acid (PLA) bio-composites for producing ceiling fan blades. Jute fabrics were treated through various chemical processes including alkali treatment, acetylation, silane treatment, and maleic anhydride treatment to improve their tensile properties and adhesion to the PLA matrix. Composites were produced with different numbers of fabric layers and tested for their mechanical properties. Test results showed that alkali+silane treated jute fabric composites exhibited the highest tensile strength and modulus. The produced bio-composite blades were found to reduce the weight and power consumption of ceiling fans compared to traditional aluminum blades.
High Volume Instrument in QC Department of Textile IndustryMohiuddin Chowdhury
The testing of fibers was always of importance to the spinner. It is done by the HVI machine. High volume instrument systems are based on the fiber bundle strength testing, ie, many fibers are checked at the same time and their average values determined. Traditional testing using micronaire, pressley, stelometre, and fibrous graph are designed to determine average value for a large number of fibers, the so called fibre bundle tests. In HVI, the bundle testing method is automated. Here, the time for testing is less and so the number of samples that could be processed is increased, quite considerably. The influence of operator is reduced.
Handle of cotton: wool knitted khadi fabriciosrjce
Hand of cotton: woolhand knitted fabrics has been reported in this study. Indian crossbred wool
(Rambouillet and Chokla) was blended with cotton (Mech I) in three different ratios (10-90%, 20-80% and 30-
70%) and yarns were prepared on hand spinning system.Knitted fabric samples were constructed on 10-12
gauge, flat bed hand knitting machine. Fabric handle was objectively assessed by SiroFAST.
The document discusses the fibrograph machine, which analyzes cotton fiber length and uniformity. It scans fiber samples using photoelectric cells and produces a length-frequency curve called a fibrograph. Key information included:
- The fibrograph prepares fiber samples using a comb to pick fibers randomly from a cylinder sample.
- It optically scans the fibers from base to tip to analyze fiber length parameters like mean length and uniformity index.
- These measurements provide objective and reproducible analysis of fiber length and uniformity compared to other testing methods.
- The document outlines the machine components, testing process, data analysis, and limitations of the fibrograph method.
This document provides details on testing the length and uniformity of cotton fibers using a Fibrograph instrument. It describes the apparatus, sampling and specimen preparation methods, testing procedures, calculations, and reporting of results. Key details include:
- The Fibrograph scans fiber samples to create a fibrogram curve from which fiber length and uniformity measurements are derived.
- Samples are prepared by combing fibers randomly onto combs and inserting them into the Fibrograph.
- The instrument measures the span lengths at specific percentage points along the fibrogram curve, from which average lengths and a uniformity ratio are calculated.
The document discusses various types of textile testing instruments used to test quality at different stages of textile production. It introduces instruments like the GSM cutter, Martindale abrasion and pilling tester, air permeability tester, lea strength tester, Uster evenness tester, wrinkle recovery tester, crease recovery tester, yarn count tester, yarn twist tester, standards tumble dryer, lab conditioner, fabric thickness gauge, yarn strength tester, and tearing strength tester. It provides details on how each instrument works and the procedures to test quality parameters like weight, abrasion resistance, air permeability, strength, evenness, wrinkle recovery, thickness, and tearing strength.
This slideshow briefs about the need for testing textiles with an example and images that can be supportive to understand. This will be the first of the presentation that leads to fiber, yarn and fabric tests as separate presentations
The document discusses textile spinning and quality control processes. It describes the key steps in textile spinning which include: yarn production from staple fibers using drawing and twisting; filament yarn production by forcing fiber-forming substances through spinnerets. The main processes are: blowroom preparation, carding, drawing, roving and ring spinning. Quality is ensured through testing of raw materials and processes. Fiber properties like length, strength and uniformity are evaluated. Machines are also tested to minimize count variations and improve yarn evenness and strength in the final product.
Quality is a relative term. It means customer needs is to be satisfied. Quality is of prime importance in any aspect of business. Customers demand and expect value for money. As producers of apparel there must be a constant endeavor to produce work of good quality. To assess the quality of textile product Textile Testing is very important work or process. Testing In response to ever-changing governmental regulations and the ever-increasing consumer demand for high quality, softlines testing and textile testing help to minimize risk and protect the interest of both manufacturers and consumers. It is important that testing is not undertaken without adding some benefit to the final product.
This PPT are used for textile engineering students, textile technology who takes textile testing courses. the PPt prepared from different books and NPTEL textile engineering web site.
Fabric softness evaluation by fabric extractionPawan Gupta
This presentation includes my research work done during my M.tech. In this i summarised the functional working of Fabric Feel Tester. In future my research gives an idea for replacing subjective assessment of fabric feel in textile processing industry.
The document discusses the USTER HVI 1000, a cotton fiber testing instrument produced by USTER. It measures important cotton fiber properties and has been the global reference tool for cotton classification for over 30 years. The USTER HVI 1000 can test up to 3,700 samples in an 8-hour shift and measures fiber attributes like micronaire, length, strength, color and trash content. It provides accurate and reliable results that are independent of the operator.
The document discusses the USTER HVI 1000 system for testing cotton quality. It analyzes the key components of the system and how it determines various quality characteristics within seconds. The HVI 1000 measures fiber length, uniformity index, micronaire value, strength, elongation, color, trash count/grade and other factors. It provides a fast, objective replacement for human cotton classers and allows farmers, traders, researchers and spinners to efficiently evaluate cotton quality. The HVI 1000 has become a universal standard in the cotton industry for classifying cotton quality.
Graphene is a material that attracts attention in technical textile applications as in many other areas due to its outstanding features. In this study, it was aimed to investigate the performance properties of graphene coated fabrics. Pre-treated polyester fabrics were coated with nano-graphene powders at different concentration rates (50, 100 and 200 g/kg) by knife-over-roll technique. According to test results, generally, the graphene coating had a positive effect on the performance properties of polyester fabrics.
This document describes Abdullah Al Mahfuj's profile and a presentation on measuring fabric stiffness. It introduces stiffness as a fabric property related to its ability to stand without support. The Shirley stiffness tester is described as an instrument used to measure fabric stiffness by determining the bending length of a fabric sample placed on an angled platform. The document provides specifications for the Shirley stiffness tester and describes the procedure to measure the bending length of cotton fabric samples in the warp and weft directions. The results show the bending length is 2.66 cm in the warp direction and 2.51 cm in the weft direction.
The contents are written in a way that the student understands the basics tests that are done to evaluate the textile fibers. In specific the properties namely length, strength, maturity and elongation.
Textile testing involves measuring properties and characteristics of textiles using techniques, tools, instruments, and machines in the laboratory. It is important for quality control in the textile industry and helps establish standards and specifications. There are various sampling techniques used for textile testing depending on the form of the material (fiber, yarn, fabric), amount of material, type of test, and information required. Random sampling aims to select samples randomly to represent the bulk material, while biased sampling may be influenced by other factors. Common fiber sampling techniques include squaring, cut squaring, and zoning to select representative samples.
This document discusses cotton fiber length, including factors that affect it, methods to measure it, and its influence on yarn and fabric properties. It describes how cotton length is determined by genetics and affected by environmental conditions. Methods to measure length include direct measurement of single fibers or preparing fiber bundles. Longer fibers contribute to stronger yarns and fabrics with better uniformity, less hairiness, and improved handle and luster. Shorter fibers increase processing waste and yield weaker, less even yarns.
This document discusses various types of textile testing instruments. It begins by explaining why quality testing is important for the textile industry in Bangladesh. It then lists the main reasons for textile testing such as checking raw materials and monitoring production. The document proceeds to describe different types of textile tests including mechanical, physical, chemical and product tests. It provides examples of specific instruments used such as the crimp rigidity tester, GSM cutters, and tensile testing machines. The document discusses the features and uses of several key textile testing instruments.
This document discusses various characteristics of yarn that are tested, including linear density, twist, yarn evenness, hairiness, bulk, and friction. It provides details on different systems for measuring linear density, the types and importance of twist, factors that affect hairiness, and methods for measuring and recovering from yarn hairiness. The document was submitted by five students - Md Mahmud Mia, Imran Hasan, Hasan Al Mamun, Mahabubur Rahman, and Naiemmur Rahman.
Recent development in needle punching nonwoven manufacturingVijay Prakash
This document provides details on the production and testing of nonwoven fabrics made from reclaimed fibers using needle punching. It describes the methodology used, including refiberizing textile waste into fibers, forming a web through carding or air laying, needle punching the web to bond the fibers, and optionally calendering the fabric. It discusses how machine parameters like needle density and punching speed influence fabric properties. The properties tested include thickness, strength, bonding strength between layers, and air permeability. The goal is to compare nonwovens made from reclaimed fibers to ordinary nonwovens and evaluate their potential use in filtration applications.
IRJET- Effect of Layering on Thermal Comfort of NonwovensIRJET Journal
This document studies the effect of layering on the thermal comfort properties of nonwoven fabrics. Four nonwoven fabric samples were produced with a varying number of layers (1 to 4 layers) but keeping the final gram per square meter (GSM) the same. Thermal properties including thermal insulation value (TIV), clo value, and water vapor transport rate were tested. The results showed that increasing the number of layers significantly improved the TIV and clo values due to an increase in the quantity of trapped air pockets. However, the maximum temperature felt (q-max) remained the same. Water vapor transport rate generally decreased with more layers due to separation of layers forming discontinuous channels, except for one sample where needle placement
The document summarizes a study that investigated the dimensional characteristics of seam puckering and the influence of various causes on puckering. An objective image processing-based assessment method was developed to quantify puckering dimensions and overcome subjective evaluations. Experiments varied needle tension, stitch density, and fabric properties to analyze their effects on puckering severity. Puckering images were processed to estimate parameters for a luminosity model characterizing dimensional puckering properties. Results showed puckering severity increased with higher tension and correlated with fabric weight and bending rigidity properties.
This paper deals with the result of an investigation by using different count yarn but same
parameters of knitting machine to produce cotton-elastane single jersey fabric. Here,the all parameters of
knitting machine including gauge, dia ,Stitch length, rpm, machine tension etcare same. Dyeing process also
carried out at same parameter for all fabrics. Finishing process like Heat setting, Stentering, compacting are
done in same condition But we use different count cotton yarn. In this paper, we mainly deal with the physical
properties of single jersey cotton fabric. we try to identify how the properties of single jersey knitted fabric like
fabric diameter(gray& finished condition) ,WPI&CPI(gray& finished condition),Fabric GSM(gray& finished
condition),Shrinkage (%) length &width wise, spiralityare changing with Count .Finally the findings are as
expected with some variation with the result that are thought theoretically.
Effect of machine parameters on knit fabric specificationstawfik_hussein
This document summarizes research on the effect of machine parameters on knit fabric specifications. The research investigated cotton knit fabrics produced with different yarn counts, machine gauges, and machine diameters. The following relationships were identified:
1) Yarn count typically increases with machine gauge. Higher gauges accommodate finer yarns.
2) The diameter of finished fabrics varies with machine diameter and fabric grammage. At different grammage ranges, the relationship between machine and fabric diameters differs.
3) A constant, Kv, relates the VDQ pulley number, stitch length, and needle number. Kv depends on machine type and diameter. It allows for better selection of VDQ number based on stitch
Implementation of Six Sigma: A case in Textile IndustrySayeef Khan
This document summarizes a study that implemented Six Sigma to improve the process of a textile company experiencing warp yarn ruptures at a rate of 4.85% defective products. The researchers applied the DMAIC process, identifying the main causes as weaving parameters. A 24 factorial design experiment optimized the parameters, significantly reducing defects. The key parameters of warp yarn tension and harness frame height were found to most influence the defect rate. The new optimized parameters reduced defects compared to the original settings.
This document summarizes a student project on analyzing the effects of different parameters on spirality in single jersey knitted fabrics. The students measured spirality in fabrics produced under varying machine settings, yarn properties, and fabric constructions. Their results showed that spirality increases with stitch length, yarn count, and the product of count and stitch length. Spirality decreases with higher fabric GSM and tighter fabric construction as measured by tightness factor. Equations were also presented from previous research relating spirality to twist factor, tightness factor, and stitch length.
IRJET- Real Time Vision System for Thread Counting in Woven FabricIRJET Journal
This document presents a real-time vision system for automatically counting threads in woven fabrics. It begins with an introduction to woven fabrics and the traditional manual method of counting threads, which is time-consuming and prone to errors. It then describes a proposed automated system using image processing techniques like blob detection and feature matching to track fabric motion and recognize warp and weft counts in real-time with high accuracy. The system is tested on denim fabric and is able to accurately count the number of warp and weft threads in the sample image. The automated approach provides an improvement over manual counting by reducing labor costs and eliminating human errors.
EFFECT OF TM AND LOOP LENGTH ON DRAPE CO-EFFICIENT OF SINGLE JERSEY KNITTED F...IAEME Publication
These are the days twist plays vital role in the hosiery yarn. The end applications of the knitted fabric are mainly depend upon the TM of Yarn. In this Research work cotton yarn and polyester cotton Blended (65%+35+) yarn used spun with same count of 30 Ne produced on Ring and compact spinning machines. Three TM levees are selected 3.32, 3.66, 3.94, to produce yarn on both spinning system. Three different loop lengths like, 0.27, 0.30 and 0.33 are selected produce single jersey plain knitted fabrics. Gamut of properties are studied with respect to geometrical and drape co-efficient of fabrics. The samples are washed for five cycles.
The document discusses textile spinning and quality control processes. It describes the key steps in textile spinning which include: yarn production from staple fibers using drawing and twisting; filament yarn production by forcing fiber-forming substances through spinnerets. The main processes are: blowroom preparation, carding, drawing, roving and ring spinning. Quality is ensured through testing of raw materials and processes. Fiber properties like length, strength and uniformity are evaluated. Machines are also tested to minimize count variations and improve yarn evenness and strength in the final product.
Quality is a relative term. It means customer needs is to be satisfied. Quality is of prime importance in any aspect of business. Customers demand and expect value for money. As producers of apparel there must be a constant endeavor to produce work of good quality. To assess the quality of textile product Textile Testing is very important work or process. Testing In response to ever-changing governmental regulations and the ever-increasing consumer demand for high quality, softlines testing and textile testing help to minimize risk and protect the interest of both manufacturers and consumers. It is important that testing is not undertaken without adding some benefit to the final product.
This PPT are used for textile engineering students, textile technology who takes textile testing courses. the PPt prepared from different books and NPTEL textile engineering web site.
Fabric softness evaluation by fabric extractionPawan Gupta
This presentation includes my research work done during my M.tech. In this i summarised the functional working of Fabric Feel Tester. In future my research gives an idea for replacing subjective assessment of fabric feel in textile processing industry.
The document discusses the USTER HVI 1000, a cotton fiber testing instrument produced by USTER. It measures important cotton fiber properties and has been the global reference tool for cotton classification for over 30 years. The USTER HVI 1000 can test up to 3,700 samples in an 8-hour shift and measures fiber attributes like micronaire, length, strength, color and trash content. It provides accurate and reliable results that are independent of the operator.
The document discusses the USTER HVI 1000 system for testing cotton quality. It analyzes the key components of the system and how it determines various quality characteristics within seconds. The HVI 1000 measures fiber length, uniformity index, micronaire value, strength, elongation, color, trash count/grade and other factors. It provides a fast, objective replacement for human cotton classers and allows farmers, traders, researchers and spinners to efficiently evaluate cotton quality. The HVI 1000 has become a universal standard in the cotton industry for classifying cotton quality.
Graphene is a material that attracts attention in technical textile applications as in many other areas due to its outstanding features. In this study, it was aimed to investigate the performance properties of graphene coated fabrics. Pre-treated polyester fabrics were coated with nano-graphene powders at different concentration rates (50, 100 and 200 g/kg) by knife-over-roll technique. According to test results, generally, the graphene coating had a positive effect on the performance properties of polyester fabrics.
This document describes Abdullah Al Mahfuj's profile and a presentation on measuring fabric stiffness. It introduces stiffness as a fabric property related to its ability to stand without support. The Shirley stiffness tester is described as an instrument used to measure fabric stiffness by determining the bending length of a fabric sample placed on an angled platform. The document provides specifications for the Shirley stiffness tester and describes the procedure to measure the bending length of cotton fabric samples in the warp and weft directions. The results show the bending length is 2.66 cm in the warp direction and 2.51 cm in the weft direction.
The contents are written in a way that the student understands the basics tests that are done to evaluate the textile fibers. In specific the properties namely length, strength, maturity and elongation.
Textile testing involves measuring properties and characteristics of textiles using techniques, tools, instruments, and machines in the laboratory. It is important for quality control in the textile industry and helps establish standards and specifications. There are various sampling techniques used for textile testing depending on the form of the material (fiber, yarn, fabric), amount of material, type of test, and information required. Random sampling aims to select samples randomly to represent the bulk material, while biased sampling may be influenced by other factors. Common fiber sampling techniques include squaring, cut squaring, and zoning to select representative samples.
This document discusses cotton fiber length, including factors that affect it, methods to measure it, and its influence on yarn and fabric properties. It describes how cotton length is determined by genetics and affected by environmental conditions. Methods to measure length include direct measurement of single fibers or preparing fiber bundles. Longer fibers contribute to stronger yarns and fabrics with better uniformity, less hairiness, and improved handle and luster. Shorter fibers increase processing waste and yield weaker, less even yarns.
This document discusses various types of textile testing instruments. It begins by explaining why quality testing is important for the textile industry in Bangladesh. It then lists the main reasons for textile testing such as checking raw materials and monitoring production. The document proceeds to describe different types of textile tests including mechanical, physical, chemical and product tests. It provides examples of specific instruments used such as the crimp rigidity tester, GSM cutters, and tensile testing machines. The document discusses the features and uses of several key textile testing instruments.
This document discusses various characteristics of yarn that are tested, including linear density, twist, yarn evenness, hairiness, bulk, and friction. It provides details on different systems for measuring linear density, the types and importance of twist, factors that affect hairiness, and methods for measuring and recovering from yarn hairiness. The document was submitted by five students - Md Mahmud Mia, Imran Hasan, Hasan Al Mamun, Mahabubur Rahman, and Naiemmur Rahman.
Recent development in needle punching nonwoven manufacturingVijay Prakash
This document provides details on the production and testing of nonwoven fabrics made from reclaimed fibers using needle punching. It describes the methodology used, including refiberizing textile waste into fibers, forming a web through carding or air laying, needle punching the web to bond the fibers, and optionally calendering the fabric. It discusses how machine parameters like needle density and punching speed influence fabric properties. The properties tested include thickness, strength, bonding strength between layers, and air permeability. The goal is to compare nonwovens made from reclaimed fibers to ordinary nonwovens and evaluate their potential use in filtration applications.
IRJET- Effect of Layering on Thermal Comfort of NonwovensIRJET Journal
This document studies the effect of layering on the thermal comfort properties of nonwoven fabrics. Four nonwoven fabric samples were produced with a varying number of layers (1 to 4 layers) but keeping the final gram per square meter (GSM) the same. Thermal properties including thermal insulation value (TIV), clo value, and water vapor transport rate were tested. The results showed that increasing the number of layers significantly improved the TIV and clo values due to an increase in the quantity of trapped air pockets. However, the maximum temperature felt (q-max) remained the same. Water vapor transport rate generally decreased with more layers due to separation of layers forming discontinuous channels, except for one sample where needle placement
The document summarizes a study that investigated the dimensional characteristics of seam puckering and the influence of various causes on puckering. An objective image processing-based assessment method was developed to quantify puckering dimensions and overcome subjective evaluations. Experiments varied needle tension, stitch density, and fabric properties to analyze their effects on puckering severity. Puckering images were processed to estimate parameters for a luminosity model characterizing dimensional puckering properties. Results showed puckering severity increased with higher tension and correlated with fabric weight and bending rigidity properties.
This paper deals with the result of an investigation by using different count yarn but same
parameters of knitting machine to produce cotton-elastane single jersey fabric. Here,the all parameters of
knitting machine including gauge, dia ,Stitch length, rpm, machine tension etcare same. Dyeing process also
carried out at same parameter for all fabrics. Finishing process like Heat setting, Stentering, compacting are
done in same condition But we use different count cotton yarn. In this paper, we mainly deal with the physical
properties of single jersey cotton fabric. we try to identify how the properties of single jersey knitted fabric like
fabric diameter(gray& finished condition) ,WPI&CPI(gray& finished condition),Fabric GSM(gray& finished
condition),Shrinkage (%) length &width wise, spiralityare changing with Count .Finally the findings are as
expected with some variation with the result that are thought theoretically.
Effect of machine parameters on knit fabric specificationstawfik_hussein
This document summarizes research on the effect of machine parameters on knit fabric specifications. The research investigated cotton knit fabrics produced with different yarn counts, machine gauges, and machine diameters. The following relationships were identified:
1) Yarn count typically increases with machine gauge. Higher gauges accommodate finer yarns.
2) The diameter of finished fabrics varies with machine diameter and fabric grammage. At different grammage ranges, the relationship between machine and fabric diameters differs.
3) A constant, Kv, relates the VDQ pulley number, stitch length, and needle number. Kv depends on machine type and diameter. It allows for better selection of VDQ number based on stitch
Implementation of Six Sigma: A case in Textile IndustrySayeef Khan
This document summarizes a study that implemented Six Sigma to improve the process of a textile company experiencing warp yarn ruptures at a rate of 4.85% defective products. The researchers applied the DMAIC process, identifying the main causes as weaving parameters. A 24 factorial design experiment optimized the parameters, significantly reducing defects. The key parameters of warp yarn tension and harness frame height were found to most influence the defect rate. The new optimized parameters reduced defects compared to the original settings.
This document summarizes a student project on analyzing the effects of different parameters on spirality in single jersey knitted fabrics. The students measured spirality in fabrics produced under varying machine settings, yarn properties, and fabric constructions. Their results showed that spirality increases with stitch length, yarn count, and the product of count and stitch length. Spirality decreases with higher fabric GSM and tighter fabric construction as measured by tightness factor. Equations were also presented from previous research relating spirality to twist factor, tightness factor, and stitch length.
IRJET- Real Time Vision System for Thread Counting in Woven FabricIRJET Journal
This document presents a real-time vision system for automatically counting threads in woven fabrics. It begins with an introduction to woven fabrics and the traditional manual method of counting threads, which is time-consuming and prone to errors. It then describes a proposed automated system using image processing techniques like blob detection and feature matching to track fabric motion and recognize warp and weft counts in real-time with high accuracy. The system is tested on denim fabric and is able to accurately count the number of warp and weft threads in the sample image. The automated approach provides an improvement over manual counting by reducing labor costs and eliminating human errors.
EFFECT OF TM AND LOOP LENGTH ON DRAPE CO-EFFICIENT OF SINGLE JERSEY KNITTED F...IAEME Publication
These are the days twist plays vital role in the hosiery yarn. The end applications of the knitted fabric are mainly depend upon the TM of Yarn. In this Research work cotton yarn and polyester cotton Blended (65%+35+) yarn used spun with same count of 30 Ne produced on Ring and compact spinning machines. Three TM levees are selected 3.32, 3.66, 3.94, to produce yarn on both spinning system. Three different loop lengths like, 0.27, 0.30 and 0.33 are selected produce single jersey plain knitted fabrics. Gamut of properties are studied with respect to geometrical and drape co-efficient of fabrics. The samples are washed for five cycles.
Effect of tm and loop length on drape co efficient of single jersey knitted f...IAEME Publication
This document summarizes research on the effect of twist multiplier (TM) and loop length on the drape coefficient of single jersey knitted fabrics produced from ring and compact spun cotton and polyester-cotton blend yarns. Samples were produced with three TM levels and three loop lengths and tested for geometrical and drape properties both initially and after five wash cycles. Results showed that TM and loop length influence drape coefficient and construction, with TM 3.32 having the most effect for cotton yarns. Drape coefficient values changed more after washing for compact spun yarns compared to ring spun yarns. The research concluded that yarn production method and knitting parameters impact fabric drape properties.
An Experimental Investigation of the Effects of Some Process Conditions on Ri...iosrjce
This study investigated the effects of eight process conditions on ring yarn breakage using a Plackett-Burman experimental design. The conditions tested were blend ratio, waste extraction percentage, roving twist, break draft, spacer size, top roller pressure, spindle speed, and yarn count. Twelve experiments were conducted at two levels for each condition. Analysis of variance found that waste extraction percentage, roving twist, top roller pressure, and spindle speed significantly affected yarn breakage. End breakage decreased with higher waste extraction and increased with higher roving twist, top roller pressure, and spindle speed. Interaction plots showed waste extraction was more important at higher spindle speeds. Finer yarns required cleaner cotton. Higher ro
1. The study investigated the spinning limits and yarn properties of cotton, viscose, and polyester fibers spun on a Dref-3 friction spinning machine across different yarn counts.
2. The spinning limit, defined as the finest yarn count that can be spun with acceptable quality and breakage rate, was found to depend on fiber type and ranged from 33-311 tex for the fibers studied.
3. As yarn count decreased, yarn properties like unevenness and imperfections generally increased for all fiber types due to poorer fiber separation and increased irregularities introduced during drafting. Twists also increased with finer counts.
4. Tensile properties responded differently for different fibers - they remained fairly constant
Effect of count and stitch length on spirality of single jersey knit fabriceSAT Journals
Abstract
The following paper focuses on change in spirality due to stitch length and count variation .This work was carried out with 12 samples of single jersey knit fabrics which were scoured and bleached with NaOH and H2O2 (35% strength), dyed with reactive dye (Remazol Yellow RR reactive class) and were finished as standard procedure . After finishing the samples were tested for spirality and compared between different stitch length and count. The result obtained in this research indicated that spirality increases strongly due to increase of stitch length when count of yarn is fixed and on fixed stitch length spirality increases with the increment of count.
Keywords: Spirality, Count, Stitch length.
Effect of count and stitch length on spirality of single jersey knit fabriceSAT Publishing House
This study examined the effect of yarn count and stitch length on spirality in single jersey knit fabrics. 12 fabric samples were produced with variations in count (30-40 Ne) and stitch length (2.6-2.95 mm). The samples were tested for spirality after scouring, bleaching, dyeing and finishing. The results showed that spirality increased as stitch length increased due to more yarn twisting. Spirality also increased with higher yarn counts due to less fabric compactness and more loops available for twisting. In conclusion, using lower yarn counts and stitch lengths can help manufacturers reduce spirality issues in knitted fabrics.
Efficiency losses calculation and identify causes of losses of circular knitt...Elias Khalil (ইলিয়াস খলিল)
This thesis deals with a major problem of production loss of a knitting industry. The knitting machine has to stop when defects occurred and then faults are corrected, which results in time loss and efficiency loss. Not only that the knitted fabric may be rejected if quality requirements are not met. An effective monitoring is required to avoid defects and to avoid productivity and quality losses. The study identifies two main categories of defects (average time required for correcting defects and machine down time) are responsible for reducing productivity. The thesis reflects that due to yarn breakage machine stopped for seen minutes per days, for maintaining machine stopped for two hours per month, for needle breakage six minutes per day and for technical problem machine stopped for several times.
The document provides information on the physical properties of raw cotton including fiber length, fineness, strength, cleanliness, and chemical deposits. It then discusses the components and processes of a blow room line. The key goals of the blow room are to open compressed cotton fibers with minimal damage, remove impurities, and create an evenly blended sliver. Common blow room machines include bale openers, mixers, cleaners, and scutchers which use beaters, grids, and air flow to open, clean, and blend the fibers into a uniform lap for input to the carding process.
Effect of Cotton Fibers and Their Trash Characteristics on the Performance of...IJERA Editor
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Regression model development for showing relation between mechanical yarn stretch & warp cmpx in looms using ANOVA model
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Regression model development for showing relation between mechanical yarn
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model Regression model developmen...
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2. Ethiopian Journal of Textile and Apparel (EJTA) Vol 1. No 2, 2020
Regression model development for showing relation between mechanical yarn stretch (%) in sizing and Warp yarn breakage (cmpx) in looms using ANOVA model
1
Regression model development for showing relation between
mechanical yarn stretch (%) in sizing and Warp yarn
breakage (cmpx) in looms using ANOVA model
Neway Seboka
Ethiopian Textile Industry Development Institute, Addis Abeba, Ethiopia
(*
Author for correspondence: contny@gmail.com)
Abstract: Mechanical yarn stretch (%) in sizing process is amongst one of the process control parameters
affecting warp yarn breakages in looms. This study tries to develop a model showing the relation between
mechanical yarn stretch percentages with warp yarn breakages in loom shed. For that experimentation is
done in Bahir Dar textile sh. Company weaving mill using cotton yarn of 20’s used for making of
20’s*20’s/24*24 (threads/cm) of fabric particulars or bed sheet article. For sizing, maize starch is used &
the main sizing parameter measurements have been kept equal other than mechanical yarn stretch
percentage, having four different levels of treatments taken randomly. These differently treated4 weavers’
beams are loaded on 4 picanol air-jet looms having same loom settings and monitored by same weaver
group. The loom-shed is kept to have an R.H of 60% and temperature of 24o
c. The experimentation follows
a single factor ANOVA with 4 levels of mechanical yarn stretch percentages and 10 replicates. Using
significance level of α = 0.05, the experimentation shows mechanical yarn stretch percentage significantly
affects the warp yarn breakages in looms. For comparing pairs of treatment means that significantly differs,
a multiple comparison method is used. Other than treatment means between the first and second level of
treatments, the rest pairs of treatment means are significantly different. From the experiment, a correlation
coefficient (R2) of 84.4% is computed. In this study, plot for predicted versus residual value is also made,
which shows the model is adequate and fitting.
Keywords: Stretch%, warp yarn cmpx, cotton yarn, ANOVA, linear regression model
1. INTRODUCTION
The process of sizing imposes stresses along
the yarn path. The stresses occurring in sizing
machine varies among the different machine
parts, i.e. between creel and squeezing roller,
at the drying cylinders, at the head stock,
during winding of the weavers’ beam, etc.
Because of the stated acting forces, the warp
yarn in sizing operation stretches. It is
described as mechanical yarn stretch and its
unit is in percentage. The most critical issue
in sizing is to control the yarn stretch. As
yarns pass through the long path from creel
to head stock, the tension applied in the
process will tend to elongate it. If this
elongation is not controlled, the deformation
so introduced will be permanently set in the
yarn (Bhuvenesh et al., 2004).
The control of yarn elongation (stretch)
between the squeezing rolls of the size box
and the first drying cylinder is critical, since
the wet yarns under high heat undergo
stretching even at low tensions. This must be
controlled by proper selection of the drive
system, such as digital or variable speed
differential transmission between the size
box and the drying unit (Gandhi, 2012). The
tension develops when the yarn is passed
3. Ethiopian Journal of Textile and Apparel (EJTA) Vol 1. No 2, 2020
Regression model development for showing relation between mechanical yarn stretch (%) in sizing and Warp yarn breakage (cmpx) in looms using ANOVA model
2
through the drying cylinders for ensuring
proper drying. The surface speeds of all the
drying cylinders should be controlled and if
they are uniform, no stretch will develop in
the drying zone (Bhuvenesh et al., 2004).
A uniform stretch from section beam to
section beam throughout the warp must be
maintained. For sizing machines, which
the stretch control % is clearly shown on the
machine’s control panel, we can directly take
the figure as mechanical yarn stretch %. But
suppose the machine is old model and no
control panel is there, we have to calculate
the mechanical yarn stretch % in sizing as per
the SOP (standard operating procedure)
stated below:
SOP to check the stretch % [the below stated
operating procedures are followed & taken
by many textile factories as standard
procedures]. Purpose of checking stretch %:
to reduce elongation loss % and reduction of
warp yarn breakage in loom shed:
• Back meter counter must be fixed at
back of the sow box,
• At starting of the beam set the front
machine counter at ‘0’
• At the same time set the back meter
counter also at ‘0’
• At the time of completion of the beam
note & record the reading of the front
machine counter,
• At the same time note & record the
reading of the back meter counters,
The stretch % to be calculated as follows:
Stretch %=Front counter reading-back
counter reading/back counter
reading*100[the stated formula is followed &
taken by many of the textile factories as
standard working formula]
2. MATERIALS AND METHODS
2.1. Materials
The experiment is conducted in Bahir Dar
textile factory with the use of cotton yarn
having the following details: for the
experiment cotton yarn of 20’s which is
produced in open end-spinning has been
used. Tables 1 & 2 most important sample
fibre properties and yarn parameters, that has
been used for the experimentation has been
stated in detail (Company dataset).
Table 1: Sample cotton fibre parameters
S.no Length
(mm)
Short
fibre
content
(%)
Moisture
content
(%)
Micronaire
(%)
Strength
(CN/tex)
Maturity
(%)
Elongation
(%)
Trash
content
(%)
1 26 8 8 4.0 27 86 6.7 4
4. Ethiopian Journal of Textile and Apparel (EJTA) Vol 1. No 2, 2020
Regression model development for showing relation between mechanical yarn stretch (%) in sizing and Warp yarn breakage (cmpx) in looms using ANOVA model
3
Table 2: Sample raw cotton yarn parameters
S.no Yarn
type
Type of
Spinning
machine
Count Elongation
(%)
Yarn
twist
level
(TPM)
Strength
(CN/tex)
Evenness
(U %)
Imperfection
level
1 Cotton OE 20’s 5.7 900 9.5 11 Thin place= -
50%
7/kmThick
place = + 50%,
40/kmNeps =
+280% 9/km
2 Cotton OE 20’s 5.7 900 9.5 11
3 Cotton OE 20’s 5.7 900 9.5 11
4 Cotton OE 20’s 5.7 900 9.5 11
Table 3: Sample sized cotton yarn and fabric particular parameters
S.no Type of
size
material
used
Size-
pickup
%
Size
viscosity
(seconds)
R.F
(concentration)
%
Moisture
content
(%)
Stretch
(%)
Fabric
particulars
1 Maize
starch
6% 13 5 7 1.34
20’s*20’s/24
(EPC)*24
(PPC), bed sheet
product
2 Maize
starch
6% 13 5 7 1.62
3 Maize
starch
6% 13 5 7 5
4 Maize
starch
6% 13 5 7 1
Table 4: Picanol air-jet loom settings for conducting the experiment
As it is stated in the Table 3, throughout the
experiment cotton yarn of 20’s has been used
with the same size concentration (%age),
moisture content (%age), size viscosity
(seconds) and size pick-up (%age) with the use
of maize starch as a sizing ingredient. Keeping
the stated parameters, the same, mechanical
yarn stretch (%) has been varied for
experimentation. So in this research, the effect
of stretch on warp yarn breakage (warp cmpx)
in picanol air-jet looms of same weaver group
has been assed. Here, all the 4 weavers’ beams
have been loaded on 4 Picanol air-jet looms
operated by same weaver-group and all the
looms were running at 500 RPM having same
loom settings as it is stated in the above table
(Table 4) &with a loom shed condition of (R.H
of 60% and temperature of 240c).
3. RESULTS AND DISCUSSIONS
A single factor experiment with a= 4 levels of
the factor and n = 10 replicates, i.e. 40 runs of
experimentation are conducted. The selection
of treatment levels is made randomly, and in
Table 5 results are shown.
S.no
Loom speed
(rpm)
Back rest
height (cm)
Adjusted warp
tension (KN)
Front shed angle
(degrees)
Shed closing time
(degrees)
1 500 4 2.5 26 280
5. Ethiopian Journal of Textile and Apparel (EJTA) Vol 1. No 2, 2020
Regression model development for showing relation between mechanical yarn stretch (%) in sizing and Warp yarn breakage (cmpx) in looms using ANOVA model
4
Table 5: Data showing observations vs. sources of variations
Stretch
(%)
Observations (Warp cmpx) Total,
yi..
Average,
yi..
1 2.3 4.0 3.1 2.5 2.9 2.1 2.1 2.3 2.5 1.9 25.7 2.57
1.34 3.2 3.5 2.4 4.1 3.6 3.9 4.5 3.8 3.5 4.2 36.7 3.67
1.62 5.6 7.2 4.5 5.2 5.4 6.0 5.6 7.4 7.8 8.0 62.7 6.27
5 25.1 25.2 8.5 12.9 15.6 16.2 16.7 17.8 15.5 19.0 172.5 17.25
297.6 7.44
Total, yi. . = 25.7+36.7+62.7+172.5
= 297.6
Average, yi.. = (2.57+3.67+6.27+17.25)/4
= 7.44
Analysis of sum of squares
SST = 2.32
+4.02
+………..+192
- 297.62
/40
= 1606.296………………………... (1)
SSTreatments = 1/10 [25.72
+,..+172.52
]-297.62
/40
= 1355.348…………………(2)
SSError= SST - SSTreatments………………. (3)
= 1606.296-1355.348 = 250.948
Analysis of degree of freedom (DF)
Table 6 Analysis of degree of freedom (DF)
DF
Between treatments a-1, 4-1=3
Error (within
treatments)
N-a, 40-4=36
Total N-1, 40-1=39
Analysis of mean squares
MSTreatments= SSTreatments/a-1……………… (4)
= 1355.348/3= 451.7827
MSError= SSError/N-a……………………….(5)
= 250.948/36= 6.9708
Fo= MSTreatments/MSError…………………. (6)
= 451.7827/6.9708= 64.81
Suppose we select α = 0.05, the probability of
reaching the correct decision on any single
comparison is 0.95 (Klaus and Oscar, 2008).
Now we compare Fo with the distribution table
at α = 0.05 significance level. We get F0.05, 3,
36= 2.87
Fo= 64.81>2.87, so we reject Ho (null
hypothesis), which tells: µ1=µ2=….µa and
accept H1, which tells µi≠µj for at least one pair
and conclude that the treatment means differ;
that is mechanical yarn stretch % in sizing
process significantly affects the warp yarn
breakages (warp cmpx) in looms. Since it is
known that the process parameter significantly
affects the warp cmpx, process optimization
and regression model development is needed.
Table 7: ANOVA for warp yarn breakage (warp cmpx) experiment
Source of variation Sum of squares Degrees of
freedom
Mean square Fo
Mechanical yarn stretch
%
1355.348 3 451.782
64.81
Error 250.948 36 6.971
Total 1606.296 39
6. Ethiopian Journal of Textile and Apparel (EJTA) Vol 1. No 2, 2020
Regression model development for showing relation between mechanical yarn stretch (%) in sizing and Warp yarn breakage (cmpx) in looms using ANOVA model
5
Comparisons among treatment means
In this study for comparing the pairs of
treatment means that differ, a multiple
comparison method is used (Robert et al.,
2003).
Contrasts
So in this paper by using the idea of contrast
method, multiple comparisons between
treatment means are made and discussed in
detail. In this article it is seen that the four
different levels of mechanical yarn stretch
%age produces different results of warp yarn
breakages (warp cmpx). But still which
treatment level is actually causing the
difference has to be known. In this study it is
suspected that the average treatment mean
found with the first treatment level (warp yarn
stretch %age of 1) and second treatment level
(warp yarn stretch %age of 1.34) seems to
produce the same warp yarn breakage (warp
cmpx). To be sure about that hypothesis testing
is conducted and discussed in detail (George,
2008).
Ho: µ1 = µ2
H1: µ1 ≠ µ2 or equivalently,
Ho: µ1 - µ2 = 0
H1: µ1 - µ2 ≠ 0
From the experimental results it is clearly
shown that the average results for warp yarn
breakages (cmpx) found by lowest levels of
mechanical yarn stretch %age, i.e. 2.57 differs
from the average results for warp yarn
breakages (cmpx) found by highest levels of
mechanical yarn stretch %age, i.e. 17.25. So no
need of testing the below stated hypothesis:
Ho: µ1 + µ2 = µ3 + µ4
H1: µ1 + µ2 ≠ µ3 + µ4 or equivalently,
Ho: µ1 + µ2 - µ3 - µ4 = 0
H1: µ1 + µ2 - µ3 - µ4 ≠0
So now we can have the contrast as a linear
combination of parameters with the form:
C =∑ 𝑐
𝑎
𝑖=1 𝑖µ𝑖
Where the constants for contrast sum to zero,
so now the first hypothesis can be expressed in
terms of contrasts as:
Ho:∑ 𝑐
𝑎
𝑖=1 𝑖µ𝑖 = 0
H1:∑ 𝑐
𝑎
𝑖=1 𝑖µ𝑖 ≠ 0
Since the first hypothesis is followed, the
contrast constants are: c3 = c4 = 0, c1 = +1 and
c2 = -1
By using the below described two basic ways,
now testing of the hypothesis can be made.
First approach: by following t-test.
We write the contrast in terms of the treatment
averages as: C =∑ 𝑐
𝑎
𝑖=1 𝑖yi.
to= ∑ 𝑐
𝑎
𝑖=1 𝑖yi.……………………………..(7)
√MSE/n∑ 𝑐
𝑎
𝑖=1 𝑖2
, where n=10
1*2.57+(-1*3.67) +0*6.27+0*17.25
=-1.1/√(0.697 ∗ 2)= -0.93
The null hypothesis would be rejected if | to |
exceeds tα/2, N-a.
t0.025,36= 2.028
So here |t0| = 0.93, which is lower than 2.028,
the second approach uses an F test. For this, the
F0 value is computed as below:
to= ∑ 𝑐
𝑎
𝑖=1 𝑖yi.…………………………..(7)
√MSE/n∑ 𝑐
𝑎
𝑖=1 𝑖2
, where n=10
Here the null hypothesis would be rejected if
F0> Fα, 1, N-a, so F0= (-0.93)2
= 0.865 and
F0.05,1,36 = 4.11
So in both methods, the tests show null
hypothesis is accepted. So the treatment means
between the first treatment level (warp yarn
stretch %age of 1) & the second treatment level
(warp yarn stretch %age of 1.34) is not
significantly different. It is also suspected that
the average treatment mean found with the
second treatment level (warp yarn stretch
%age of 1.34) and third treatment level (warp
7. Ethiopian Journal of Textile and Apparel (EJTA) Vol 1. No 2, 2020
Regression model development for showing relation between mechanical yarn stretch (%) in sizing and Warp yarn breakage (cmpx) in looms using ANOVA model
6
yarn stretch %age of 1.62) seems to produce
the same warp yarn breakage (warp cmpx). To
be sure about that hypothesis testing is
conducted and discussed in detail:
Ho: µ2 = µ3
H1: µ2 ≠ µ3 or equivalently,
Ho: µ2 - µ3 = 0
H1: µ2 - µ3 ≠ 0
So now we can have the contrast as a linear
combination of parameters with the form:
C =∑ 𝑐
𝑎
𝑖=1 𝑖µ𝑖
Where the constants for contrast sum to zero,
so now the hypothesis can be expressed in
terms of contrasts as:
Ho:∑ 𝑐
𝑎
𝑖=1 𝑖µ𝑖 = 0
H1:∑ 𝑐
𝑎
𝑖=1 𝑖µ𝑖 ≠ 0
The contrast constants are: c1 = c4 = 0, c2 = +1
and c3 = -1
By using the below described two basic ways,
now testing of the hypothesis can be made.
First approach: by following t-test. We write
the contrast in terms of the treatment
averages as:
C =∑ 𝑐
𝑎
𝑖=1 𝑖yi.
to= ∑ 𝑐
𝑎
𝑖=1 𝑖yi
√MSE/n∑ 𝑐
𝑎
𝑖=1 𝑖2
0*2.57+1*3.67 + (-1*6.27) + 0*17.25
= -2.6/√(0.697 ∗ 2)= -2.2
The null hypothesis would be rejected if | to |
exceeds tα/2, N-a.
t0.025,36= 2.028
So here |t0| = 2.2, which exceeds 2.028,
The second approach uses an F test. For this,
the F0 value is computed as below:
F0 = t0
2
= (∑ 𝑐
𝑎
𝑖=1 𝑖yi.)2
…………(8)
MSE/n∑ 𝑐
𝑎
𝑖=1 𝑖2
Here the null hypothesis would be rejected if
F0> Fα, 1, N-a
So F0= (-2.2)2
= 4.84 and F0.05,1,36 = 4.11
So in both methods, the tests show null
hypothesis is rejected. So the treatment means
between the second treatment level (warp yarn
stretch %age of 1.34) & the third treatment
level (warp yarn stretch %age of 1.62) is
significantly different. It is also suspected that
the average treatment mean found with the
third treatment level (warp yarn stretch %age
of 1.62) and fourth treatment level (warp yarn
stretch %age of 5) seems to produce the same
warp yarn breakage (warp cmpx). To be sure
about that, hypothesis testing is conducted and
discussed in detail:
Ho: µ3 = µ4
H1: µ3 ≠ µ4 or equivalently,
Ho: µ3 - µ4 = 0
H1: µ3 - µ4 ≠ 0
So now we can have the contrast as a linear
combination of parameters with the form:
C =∑ 𝑐
𝑎
𝑖=1 𝑖µ𝑖
Where the constants for contrast sum to zero,
so now the hypothesis can be expressed in
terms of contrasts as:
Ho:∑ 𝑐
𝑎
𝑖=1 𝑖µ𝑖 = 0
H1:∑ 𝑐
𝑎
𝑖=1 𝑖µ𝑖 ≠ 0
The contrast constants are: c1 = c2 = 0, c3 = +1
and c4 = -1
By using the below described two basic ways,
now testing of the hypothesis can be made.
First approach: by following t-test.
We write the contrast in terms of the treatment
averages as: C =∑ 𝑐
𝑎
𝑖=1 𝑖yi.
to= ∑ 𝑐
𝑎
𝑖=1 𝑖yi
√MSE/n∑ 𝑐
𝑎
𝑖=1 𝑖2
0*2.57+0*3.67 + 1*6.27 + (-1*17.25)
= -10.98/√(0.697 ∗ 2)= -9.299
The null hypothesis would be rejected if | to |
exceeds tα/2, N-a.
t0.025,36= 2.028
So here |t0| = 9.299, which exceeds 2.028,
The second approach uses an F test. For this,
the F0 value is computed as below:
F0 = t0
2
= (∑ 𝑐
𝑎
𝑖=1 𝑖yi.)2
MSE/n∑ 𝑐
𝑎
𝑖=1 𝑖2
Here the null hypothesis would be rejected if
F0> Fα, 1, N-a
So F0= (-9.299)2
= 86.47 and F0.05,1,36 = 4.11
8. Ethiopian Journal of Textile and Apparel (EJTA) Vol 1. No 2, 2020
Regression model development for showing relation between mechanical yarn stretch (%) in sizing and Warp yarn breakage (cmpx) in looms using ANOVA model
7
So in both methods, the tests show null
hypothesis has to be rejected. So the treatment
means between the third treatment level (warp
yarn stretch %age of 1.62) & the fourth
treatment level (warp yarn stretch %age of 5)
is significantly different.
Process optimization and linear regression
model development (Gandhi, 2012).
The empirical model/development of linear
regression model helps for process
optimization, hence for finding the levels of
the warp yarn stretch (%age) that result in the
best values of the warp yarn breakage (warp
cmpx). Percentage (%) in sizing and warp.
yarn breakage (warp cmpx) in looms is
computed as (Max, 2011).
y= βo+β1X1+€………… (9)
β= (X'X)-1
X'y…………… (10)
So from the data achieved based on the experimentation done, we have:
X'X = 1*1+1*1+1*1+1*1
=4, 1*1+1*1.34+1*1.62+1*5
=8.96
= 1*1+1.342
+1.622
+52
= 30.42
X'y
=
1*2.57+1*3.67+1*6.27+1*17.25
= 29.76
=1*2.57+1.34*3.67+1.62*6.27+5*17.25
= 103.89
β= (X'X)-1
X'y…………………………. (10)
Now we can compute β= (X'X)-1
X'y as:
βo= (0.73*29.76) + (-0.215*103.89)
= -0.6126
β1= (-0.215*29.76) + (0.096*103.89) =
3.5755
So the linear regression model showing the
relationship between mechanical yarn stretch
percentage (%) in sizing and warp yarn
breakage (warp cmpx) in looms is computed as
(Max, 2011).:
y= βo+β1X1+€……………………………..
(9)
y = -0.6126+3.5755X1
Where y, is warp yarn breakage (warp cmpx),
X1, is mechanical yarn stretch (%) in
sizing,
So based on the regression model that has been
developed earlier, calculation of predicted
value has been made (Table 8) showing the
results.
Table 8: Results for calculated predicted value
Stretch
(%)
Observations (Warp cmpx)
Predicted
value
1 2.3 4 3.1 2.5 2.9 2.1 2.1 2.3 2.5 1.9 2.9628
1.34 3.2 3.5 2.4 4.1 3.6 3.9 4.5 3.8 3.5 4.2 4.17847
1.62 5.6 7.2 4.5 5.2 5.4 6 5.6 7.4 7.8 8 5.17961
5 25.1 25.2 8.5 12.9 15.6 16.2 16.7 17.8 15.5 19 17.2648
X= 1 1 y= 2.57 X'= 1 1 1 1
1 1.34 3.67 1 1.34 1.62 5
1 1.62 6.27
1 5 17.25
(X'X)-1
= 1/ [(4*30.42) -(8.96)2
]
* 30.42 -8.96
-8.96 4
= 0.73 -
0.215
-0.215
0.096
X'X = 4 8.96 X'y = 29.76
8.96 30.42 103.89
9. Ethiopian Journal of Textile and Apparel (EJTA) Vol 1. No 2, 2020
Regression model development for showing relation between mechanical yarn stretch (%) in sizing and Warp yarn breakage (cmpx) in looms using ANOVA model
8
Table 9: Results for Residual value
Stretc
h (%)
Residual value = observed value-predicted value
1 -0.6628 1.0372 0.1372 -0.4628 -0.0628 -0.8628 -0.8628 -0.6628 -0.4628 -1.0628
1.34 -0.97847 -0.67847 -1.77847 -0.07847 -0.57847 -0.27847 0.32153 -0.37847 -0.67847 0.02153
1.62 0.42039 2.02039 -0.67961 0.02039 0.22039 0.82039 0.42039 2.22039 2.62039 2.82039
5 7.8352 7.9352 -8.7648 -4.3648 -1.6648 -1.0648 -0.5648 0.5352 -1.7648 1.7352
Analysis of predicted versus Actual value
Figure 1: Plot for Predicted versus Actual warp cmpx value
Computation of R2
R2
= SSTreatments/SST………. …………. (11)
=1355.348/1606.296
=0.844, 84.4%
Thus, in this experiment, the factor
‘mechanical yarn stretch’ explains about 84.4
percent of the variability in warp yarn
breakage (warp cmpx). From the result of
correlation, the variance of the predicted
value explains the variance of
observed/actual value to an extent of about
84.4%.
Analysis of predicted versus residual value
So from the two plots, it is seen that the
differences between the observed warp yarn
stretch and the predicted warp yarn stretch
values are small and unbiased, i.e. anywhere
in the observation axis, the predicted values
are not shown to be systematically too
high/too low. This shows us the developed
linear regression model fits the data well.
Additionally, from the figure below (Figure
2), the residual does not follow any obvious
pattern, i.e. it is structure less& the residuals
are randomly scattered around zero. Because
of this, we can tell that the developed linear
regression model is adequate and fitting.
0
5
10
15
20
25
30
0 5 10 15 20
Actual
warp
cmpx
values
Predicted warp cmpx values
10. Ethiopian Journal of Textile and Apparel (EJTA) Vol 1. No 2, 2020
Regression model development for showing relation between mechanical yarn stretch (%) in sizing and Warp yarn breakage (cmpx) in looms using ANOVA model
9
Figure 2: Plot for Predicted versus Residual value
4. CONCLUSIONS
The experiment is done in Bahir Dar textile
sh. Company weaving mill with the use of
cotton yarn having 20’s with same size pick-
up, concentration, viscosity and moisture
content percentages is used for assessing the
effects of different level of treatments of
mechanical yarn stretch percentages on warp
yarn breakages (warp cmpx) in Picanol air-jet
looms of same weaver group having same
loom settings and running at the speed of 500
RPM. Using a single factor experiment of 4
levels of the mechanical yarn stretch
percentage and 10 replicates, i.e. 40 runs has
been made for computing ANOVA
analysis.With the use of 0.05 significance
level (α=0.05), it is proved that mechanical
yarn stretch (%) significantly affects the warp
yarn breakage (cmpx) and for comparing
pairs of treatment means that significantly
differs, a multiple comparison method with
contrasts has been made. besides treatment
means between the first and second level of
treatments, the other pairs of treatment means
are significantly different. Since this is the
case, process optimization and linear
regression model development has been
conducted. Based on the model, calculations
of predicted values and correlation
coefficient (R2
) is computed to be 84.4%. a
plot for predicted versus residual value has
also been made showing that the developed
linear regression model is adequate and
fitting.
5. REFERENCES
Bhuvenesh C. Goswami, R.D. and Anandjiwala,
D.M.H. (2004). Textile Sizing. Marcel Dekker, New
York, USA.
Gandhi, K.L. (2012). Woven Textiles Principles,
Developments and Applications. Woodhead
publishing series in Textiles.
George, C. (2008). Statistical Design. Springer
Science+Business Media, LLC.
Klaus, H. and Oscar, K. (2008). Design and Analysis
of Experiments: Introduction to Experimental Design.
John Wiley & Sons, New Jersey, USA.
Max, D.M. (2011). Design of Experiments: An
Introduction based on Linear Models. Taylor and
Francis Group, LLC.
Montgomery, D.C. (2012). Design and Analysis of
Experiments. John Wiley & Sons, New Jersey, USA.
Robert, L.M., Richard, F.G.and James, L.H. (2003).
Statistical Design and Analysis of Experiments: With
Applications to Engineering and Science. John Wiley
& Sons,New Jersey, USA.
-10
-5
0
5
10
0 5 10 15 20
Residual
value
Predicted warp cmpx values
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