| 引用本文: | 邵景峰,马创涛.一种多工序知识关联的纺纱质量智能控制模型[J].控制理论与应用,2018,35(6):840~849.[点击复制] |
| SHAO Jing-feng,MA Chuang-tao.Intelligent control model for yarn quality based on multi-process knowledge association[J].Control Theory and Technology,2018,35(6):840~849.[点击复制] |
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| 一种多工序知识关联的纺纱质量智能控制模型 |
| Intelligent control model for yarn quality based on multi-process knowledge association |
| 摘要点击 2210 全文点击 1476 投稿时间：2017-08-24 修订日期：2018-02-02 |
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| DOI编号 10.7641/CTA.2018.70601 |
| 2018,35(6):840-849 |
| 中文关键词 质量控制 多工序知识关联 质量损失函数 pareto最优 |
| 英文关键词 quality control multi-process knowledge association quality loss function pareto optimality |
| 基金项目 陕西省重点研发计划项目(2017GY-39), 西安市科技计划项目(2017074CG/RC037(XAGC005)),中国纺织工业联合会应用基础研究项目(J201508),陕西省教育厅服务地方专项计划项目(16JF009),中国纺织工业联合会科技指导性项目计划(2016076,2013068),西安工程大学研究生创新基金(CX201731) |
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| 中文摘要 |
| 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. |
| 英文摘要 |
| 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 multiprocess
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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