Quality Parameter Adaptive Optimization for Spinning Process Using Dynamic Non-Dominated Sorting Algorithm
Intelligent textile equipment can discover potential patterns in the production process through data mining, and utilize these patterns through intelligent optimization, ultimately achieving intelligent and automated textile production. This paper focuses on the spinning process parameters optimizat...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2419575 |
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| author | Di Wu Sheng Hu |
| author_facet | Di Wu Sheng Hu |
| author_sort | Di Wu |
| collection | DOAJ |
| description | Intelligent textile equipment can discover potential patterns in the production process through data mining, and utilize these patterns through intelligent optimization, ultimately achieving intelligent and automated textile production. This paper focuses on the spinning process parameters optimization under changing spinning conditions and proposes a dynamic non-dominant ranking parameter quality adaptive optimization algorithm. The factors of spinning process condition changes are transformed into mathematical dynamic constraints and constructing an adaptive optimization model for spinning parameter quality. Based on this, the response mechanism of spinning environment is established to readjust the optimization direction according to the change of spinning conditions, and the DNSGA-II is used to solve the quality adaptive optimization model. A case study is designed to validate the effectiveness, results show that for different usage periods of wire rings, the optimal breaking strength is 5.6, and the number of details is 33.3, 31.1, and 41.6 respectively. In some degree, the proposed algorithm can effectively adapt to the quality optimization problem of spinning process parameters under different spinning conditions, which could provide corresponding parameter optimization combinations for different spinning conditions. |
| format | Article |
| id | doaj-art-e64060e7e76c40dbb2f69f5594877b6f |
| institution | Kabale University |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-e64060e7e76c40dbb2f69f5594877b6f2024-12-16T16:13:01ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2419575Quality Parameter Adaptive Optimization for Spinning Process Using Dynamic Non-Dominated Sorting AlgorithmDi Wu0Sheng Hu1School of Basic Medical Science, Shaanxi University of Chinese Medicine, Xian Yang, ChinaSchool of Mechanical and Electrical Engineering, Xi’an Polytechnic University, Xi’an, ChinaIntelligent textile equipment can discover potential patterns in the production process through data mining, and utilize these patterns through intelligent optimization, ultimately achieving intelligent and automated textile production. This paper focuses on the spinning process parameters optimization under changing spinning conditions and proposes a dynamic non-dominant ranking parameter quality adaptive optimization algorithm. The factors of spinning process condition changes are transformed into mathematical dynamic constraints and constructing an adaptive optimization model for spinning parameter quality. Based on this, the response mechanism of spinning environment is established to readjust the optimization direction according to the change of spinning conditions, and the DNSGA-II is used to solve the quality adaptive optimization model. A case study is designed to validate the effectiveness, results show that for different usage periods of wire rings, the optimal breaking strength is 5.6, and the number of details is 33.3, 31.1, and 41.6 respectively. In some degree, the proposed algorithm can effectively adapt to the quality optimization problem of spinning process parameters under different spinning conditions, which could provide corresponding parameter optimization combinations for different spinning conditions.https://www.tandfonline.com/doi/10.1080/08839514.2024.2419575 |
| spellingShingle | Di Wu Sheng Hu Quality Parameter Adaptive Optimization for Spinning Process Using Dynamic Non-Dominated Sorting Algorithm Applied Artificial Intelligence |
| title | Quality Parameter Adaptive Optimization for Spinning Process Using Dynamic Non-Dominated Sorting Algorithm |
| title_full | Quality Parameter Adaptive Optimization for Spinning Process Using Dynamic Non-Dominated Sorting Algorithm |
| title_fullStr | Quality Parameter Adaptive Optimization for Spinning Process Using Dynamic Non-Dominated Sorting Algorithm |
| title_full_unstemmed | Quality Parameter Adaptive Optimization for Spinning Process Using Dynamic Non-Dominated Sorting Algorithm |
| title_short | Quality Parameter Adaptive Optimization for Spinning Process Using Dynamic Non-Dominated Sorting Algorithm |
| title_sort | quality parameter adaptive optimization for spinning process using dynamic non dominated sorting algorithm |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2419575 |
| work_keys_str_mv | AT diwu qualityparameteradaptiveoptimizationforspinningprocessusingdynamicnondominatedsortingalgorithm AT shenghu qualityparameteradaptiveoptimizationforspinningprocessusingdynamicnondominatedsortingalgorithm |