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 textiles using computer-aided multi-head embroidery machines is one of the most extensively utilised manufacturing methods in the textile and garment industries today. Because the embroidery process involves inserting a number of embroidery threads into the fabric structure, it is apparent that the basic physical and functional features of the textile-fabric are prone to significant change. Through the embroidery process on high-speed machines, the textile-fabric undergoes physical, functional, and aesthetic transformation and metamorphism. As a result, it is critical to develop algorithms or empirical equations for analysing the influence of relevant input parameters and accurately predicting the properties of the embroidered cloth relevant to its end-use in the apparel sector. In this regard, an effort has been made to explore the influences and construct a prediction equation for the prediction of longitudinal stiffness of embroidered fabric in terms of flexural rigidity in the fabric’s warp direction using linear multiple regressions. The primary goal of this study was to create an industry-friendly and straightforward empirical equation for pre-assessment and exact control of the stiffness property of embroidered materials. In this situation, the following input parameters are taken into account: 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 pattern. The final prediction model is statistically validated using new embroidered samples of various varieties and statistical methods such as residual analysis, residual plot, regression plot, R-value, R-square value, and hypothesis testing using the F-test. A very good level of prediction accuracy is obtained. In addition, the effects of needlework parameters in this context have been investigated using the related regression coefficients and three-dimensional (3D) surface curves. The most influential parameter has revealed as stitch density, followed by stitch length and stitch angle.

Author(S) Details

Anirban Dutta
Government College of Engineering & Textile Technology Serampore, 12, William Carey Road, Serampore, W.B. 712201, India.

Biswapati Chatterjee
Government College of Engineering & Textile Technology Serampore, 12, William Carey Road, Serampore, W.B. 712201, India.

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