Prediction of Sewing Thread Consumption for Over-Edge Stitches Class 500 Using Geometrical and Multi-Linear Regression Models

Malek Sarah 1 , Dominique C. Adolphe 2  and Jaouachi Boubaker 1
  • 1 National School of Engineers of Monastir, Monastir, Tunisia
  • 2 Laboratoire de Physique et Mécanique Textile EA 4365, Mulhouse, France

Abstract

Rapid and precise methods (geometrical and statistical), which aim to predict the amount of sewing thread needed to sew a garment using different over-edge stitches of class 500 (501, 503, 504, 505, 512, 514, 515, and 516), have been provided. Using a geometrical method of different over-edge stitch shapes, sewing consumption value was determined to avoid the unused stocks for each stitch type. The prediction of the sewing thread consumption relative to each investigated over-edge stitch was proposed as a function of the studied input parameters, such as material thickness, stitch density, yarn diameter, and seam width (distance between the needle and the cutter and the distance between two needles). To improve the established models using a geometrical method, a statistical method based on multi-linear regression was studied. Geometrical and statistical results were discussed, and the coefficient R2 value was determined to evaluate the accuracy of the tested methods. By comparing the estimated thread consumption with the experimental ones, we concluded that the geometrical method is more accurate than the statistical method regarding the range of R2 (from 97.00 to 98.78%), which encourages industrialists to use geometrical models to predict thread consumption.

All studied parameters contributing to the sewing thread consumption behavior were investigated and analyzed in the experimental design of interest. It was concluded that the most important parameter affecting thread consumption is the stitch density. The material thickness and the seam width (B1) have a little impact on thread consumption values. However, the seam thread diameter has a neglected effect on thread consumption.

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