Prediction of tear strength of a plain woven fabric using response surface methodology.

Abstract

Woven fabrics have been developed over the centuries to meet a wide range of requirements and end uses. They are increasingly being used in apparel, home interiors and in many technical applications such as industrial textiles, geotextiles, protective clothing and so many others. One of the most important characteristics of woven fabrics is strength which can be measured in form of tensile, tearing or bursting strength. However, tear strength is one of the most important assessment criteria of fabrics whose end uses require long service hence significant importance has to be associated with it since it is often used to directly assess the serviceability of the fabric. Quality control is usually carried out in form of standard tests to ensure that these fabrics meet the required value of tear strength specified by the consumer before they are released from the company. Many samples are manufactured to carry out the tear strength test and in case the fabric does not meet the requirements, the parameters have to be varied and the whole process is repeated over and over again until the requirements are met. This leads to wastage of material, time and increase in the production costs. Some studies of statistical models for predicting tear strength of woven fabrics have been conducted for example the use of computational and Taguchi techniques among others. Response surface methodology however can be used as an alternative technique for modelling the relationship between process inputs and output factors to predict responses and to optimize the process input factors. But so many factors are related to the fabric strength like yarn count, twist, fiber fineness, GSM, stiffness, yarn density, blend ratio, fabric structure, cover, yarn density, no. of layer, tightness factor and so on. It is very complex to establish a mathematical relation to determine strength considering all these parameters. This paper will concentrate on yarn count and yarn density (picks per inch and ends per inch).