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...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-08-01
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| Series: | AUTEX Research Journal |
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| Online Access: | https://doi.org/10.1515/aut-2025-0052 |
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| author | Khedher Faouzi Hamdi Thouraya Jaouachi Boubaker Jmali Mohamed |
| author_facet | Khedher Faouzi Hamdi Thouraya Jaouachi Boubaker Jmali Mohamed |
| author_sort | Khedher Faouzi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-65c38daecafa4e9cbf496dea54163a9a |
| institution | Kabale University |
| issn | 2300-0929 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | AUTEX Research Journal |
| spelling | doaj-art-65c38daecafa4e9cbf496dea54163a9a2025-08-20T03:46:50ZengDe GruyterAUTEX Research Journal2300-09292025-08-012511740174610.1515/aut-2025-0052Predicted sewing thread consumption using neural network method based on the physical and structural parameters of knitted fabricsKhedher Faouzi0Hamdi Thouraya1Jaouachi Boubaker2Jmali Mohamed3University of Monastir, Textile Engineering Laboratory (LGTex), LR11ES42, 5070, Ksar Hellal, TunisiaDepartment of Fashion and Textile Design, College of Arts and Design, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi ArabiaUniversity of Monastir, Textile Engineering Laboratory (LGTex), LR11ES42, 5070, Ksar Hellal, TunisiaUniversity of Monastir, Textile Engineering Laboratory (LGTex), LR11ES42, 5070, Ksar Hellal, TunisiaThis 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.https://doi.org/10.1515/aut-2025-0052knitted textilessewing threadthread consumptionchain stitchcompressibilityfoot pressurestitchingcompressive fabrics |
| spellingShingle | Khedher Faouzi Hamdi Thouraya Jaouachi Boubaker Jmali Mohamed Predicted sewing thread consumption using neural network method based on the physical and structural parameters of knitted fabrics AUTEX Research Journal knitted textiles sewing thread thread consumption chain stitch compressibility foot pressure stitching compressive fabrics |
| title | Predicted sewing thread consumption using neural network method based on the physical and structural parameters of knitted fabrics |
| title_full | Predicted sewing thread consumption using neural network method based on the physical and structural parameters of knitted fabrics |
| title_fullStr | Predicted sewing thread consumption using neural network method based on the physical and structural parameters of knitted fabrics |
| title_full_unstemmed | Predicted sewing thread consumption using neural network method based on the physical and structural parameters of knitted fabrics |
| title_short | Predicted sewing thread consumption using neural network method based on the physical and structural parameters of knitted fabrics |
| title_sort | predicted sewing thread consumption using neural network method based on the physical and structural parameters of knitted fabrics |
| topic | knitted textiles sewing thread thread consumption chain stitch compressibility foot pressure stitching compressive fabrics |
| url | https://doi.org/10.1515/aut-2025-0052 |
| work_keys_str_mv | AT khedherfaouzi predictedsewingthreadconsumptionusingneuralnetworkmethodbasedonthephysicalandstructuralparametersofknittedfabrics AT hamdithouraya predictedsewingthreadconsumptionusingneuralnetworkmethodbasedonthephysicalandstructuralparametersofknittedfabrics AT jaouachiboubaker predictedsewingthreadconsumptionusingneuralnetworkmethodbasedonthephysicalandstructuralparametersofknittedfabrics AT jmalimohamed predictedsewingthreadconsumptionusingneuralnetworkmethodbasedonthephysicalandstructuralparametersofknittedfabrics |