Study of Influences of the Process Parameters and Development of Empirical Prediction Model through Linear Multiple Regression for the Longitudinal Stiffness of Embroidered Textile Fabric

Surface decoration on textile fabrics through computer aided multi-head embroidery machines is one of the most widely used manufacturing processes in the present day, in the context of textile and garment industry. As the embroidery process includes addition of a number of embroidery-threads inside the fabric structure, it is quite obvious that basic physical and functional properties of textile-fabric are subject to alter significantly. It can be said that the textile-fabric is subject to physical, functional and aesthetic transformation and metamorphism through the embroidery process in high-speed machines. Hence, it is important to develop algorithms or empirical equations for analysis of the influence of relevant input parameters and proper prediction of the properties of the embroidered fabric, relevant to its end-use in apparel industry. In this context, an effort has been made to study the influences and develop a prediction equation through linear multiple regressions for the prediction of longitudinal stiffness of embroidered fabric in terms of flexural rigidity in warp direction of the fabric. The prime objective of this particular research was to develop an industry-friendly and simple empirical equation for pre-assessment and precise control of the stiffness property of embroidered fabrics. The input parameters considered in this case are: warp-way flexural rigidity of the base fabric, breaking load and linear density of the embroidery thread, stitch density, average stitch length and average stitch angle of the embroidery design. The final prediction model is statistically verified taking new embroidery samples of different varieties, through the statistical tools like residual analysis, residual plot, regression plot, R-value, R-square value and test of hypothesis through F-test. A very satisfactory level of prediction accuracy is obtained. Also, the influences of the embroidery parameters in this context have been analyzed through the corresponding regression coefficients and the three-dimensional (3D) surface curves. Stitch density has been emerged as the most influential parameter, followed by the stitch length and the stitch angle.