Artificial-Neural-Network-Based Predicted Model for Seam Strength of Five-Pocket Denim Jeans: A Review

Abstract

This study explores previous research efforts concerning prediction models related to the textile and polymer industry, especially garment seam strength, emphasizing critical parameters such as stitch density, fabric GSM, thread type, thread count, stitch classes, and seam types. These parameters play a pivotal role in determining the durability and overall quality of denim jeans based on cellulosic polymer. A significant focus is dedicated to the mathematical computational models employed for predicting seam strength in five-pocket denim jeans. Herein, the discussion poses the application of AI for manufacturing industries, especially for textile and clothing sectors, and highlights the importance of using a machine learning prediction model for sewing thread consumption, seam strength analysis, and seam performance analysis. Therefore, the authors suggest the significant importance of the machine learning prediction model, as future trends anticipate advancements in AI-driven methodologies, potentially leading to high-profile predictions and superior manufacturing processes. The authors also describe the limitation of AI and address a comprehensive model of risk outlines of AI in the manufacturing-based industries, especially the garments industry. Put simply, this review serves as a bridge between the realms of AI, mathematics, and textile engineering, providing a clear understanding of how artificial-neural-network-based models will be shaping the future of seam strength prediction in the denim manufacturing landscape. This type of evolution, based on ANN, will support and enhance the accuracy and efficiency of seam strength predictions by allowing models to discern intricate patterns and relationships within vast and diverse datasets.