Unveiling the dynamics of seam performance: a regression analysis approach for predictive modeling

Abstract

Abstract The performance of seams is crucial for the garment manufacturing industry's sustainability. This study delves into the factors impacting seam slippage, strength, and efficiency. By analyzing fabric specifications (weight and weave), machine settings (presser foot pressure and speed), and seam direction, the study uncovers their influence on seam properties. Samples of 100% cotton plain and twill fabrics, both of low (120 g / m 2 to140 g / m 2 ) and high (275 g / m 2 to320 g / m 2 ) weights, were chosen. Lock stitch type (301) and superimposed seam class were employed, with samples stitched in both weft and warp directions using the same stitch type, seam class, and substantial core spun sewing thread. An experimental design was created using variable parameters (fabric weight, weave, machine speed, and presser foot pressure), resulting in a 23 × 32 regression analysis. This design facilitated the development of 72 stitched samples. The regression analysis yielded an optimized solution, with square regressions indicating significant seam strength (85%) and efficiency (93%). In summary, this research provides insights into optimizing seam performance by understanding the interplay of fabric, machine, and stitching parameters, crucial for enhancing the quality and durability of garment seams.