Sensitivity analysis-based quality optimization strategy for multi-correlation parameters in spinning process

To address the problem of quality optimization of multi-correlation parameters in the spinning process, this paper proposes a new method based on a sparrow search algorithm (SSA). Firstly, a generalized regression neural network (GRNN) is used to investigate the impact of the spinning process parameters on yarn quality, and quality forward modeling in the spinning process is established. And based on the coupling and correlation characteristics of spinning process parameters, sensitivity analysis is used to analyze the influence of each spinning process parameter on yarn quality, the correlation spinning process parameters for further analysis. Then a model of quality optimization with spinning process parameters is established, and SSA is used to solve the model of quality optimization with multi-correlation parameters in the spinning process. Finally, the effectiveness of the proposed method was validated through an instance. The results show that the optimal spinning process parameters combination generation of [32.159 5.2 0.8 14.8 24.540 8588.677 21.708] occurs in a configuration with a fitness value of 0.0003. The proposed sensitivity analysis-based quality optimization strategy reveals good performances in terms of both convergence speed and optimization accuracy, which will provide guidance for improving yarn quality.