为解决单一工序的纺纱质量控制模型难以实现对纺纱质量的精准控制问题,构建了一种基于多工序知识关联的纺纱质量智能控制模型.首先,选取纱线断裂强度为主要控制指标,设计了基于纱线断裂强度的多工序质量控制点及质量损失函数,实现了棉纺生产过程中多工序质量控制点间知识的关联.进而,以质量损失函数为目标函数构建了纺纱质量控制模型,并借助自动过程控制技术实现了基于数据反馈的纺纱质量控制.然后,将惩罚函数引入到纺纱质量控制模型中,并利用多目标烟花算法对模型进行了求解.最后,通过对比验证表明,该模型与未考虑多工序间知识关联的质量控制模型以及控制前的结果相比,纱线断裂强度提升了1.27%和3.40%,纱线不合格率降低了23.48%和50.00%,从而有利于解单一工序的纺纱质量控制模型难以实现对纺纱质量的精准控制问题.%To solve the problem of yarn quality was difficult to control accurately by using quality control model based on single process, an intelligent control model for yarn quality based on multi-process knowledge association was built. Firstly, the yarn fracture strength was selected as the main control indexes, and the knowledge association among multi-process was achieved based on the quality control point and quality loss function. Furthermore, the quality loss function was selected as the objective function to built quality control model, and the automatic process control technology was adopted to achieve the quality control based on data feedback. And then, the penalty function was introduced to solve the model by using multi-object firework algorithm. Finally, as verified by the experiment, the results was shown that fracture strength was improved by 1.27%and 3.40%, and the nonconforming rate of the yarn production was decreased by 23.48%and 50.00%after comparing the results of the model we proposed with the control model ignoring multi-process knowledge association and the results before the control. Meanwhile, the comparison and analysis of the results indicate that the model we proposed was conducive to solve the problem of yarn quality was difficult to control by single process quality control model.
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