Predicted sewing thread consumption using neural network method based on the physical and structural parameters of knitted fabrics

This article presents an experimental study on the influence of various parameters on sewing thread consumption. Four knitted samples, featuring different structures and thicknesses, were tested by sewing two- and three-layer using chain stitch type 401. This work allows for examining the effects of...

詳細記述

書誌詳細
出版年:AUTEX Research Journal
主要な著者: Khedher Faouzi, Hamdi Thouraya, Jaouachi Boubaker, Jmali Mohamed
フォーマット: 論文
言語:英語
出版事項: De Gruyter 2025-08-01
主題:
オンライン・アクセス:https://doi.org/10.1515/aut-2025-0052
その他の書誌記述
要約:This article presents an experimental study on the influence of various parameters on sewing thread consumption. Four knitted samples, featuring different structures and thicknesses, were tested by sewing two- and three-layer using chain stitch type 401. This work allows for examining the effects of the sewing machine foot pressure height, surface mass, fabric thickness, and number of layers sewn on thread consumption. It was concluded that lower foot pressure results in a significant increase in the amount of thread used. In addition, accurately predicting thread consumption makes it easier to control the stock level of inventories, which is important for effective supply chain management. With optimized inventory, companies will save on storage expenses and minimize the downtime between operations, resulting in increased productivity. The combined application of neural nets and statistical techniques increases the accuracy of forecasts, which is essential for manufacturers in a highly competitive environment.
ISSN:2300-0929