Quality Parameter Adaptive Optimization for Spinning Process Using Dynamic Non-Dominated Sorting Algorithm - Di Wu; Sheng Hu (Applied Artificial Intelligence)
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.