Discrete Markov jump system based dynamic optimization method of machine tools in cloud manufacturing environment

被引：0

作者：

Li X. ^{[1
]}

Fang Z. ^{[1
]}

Yin C. ^{[1
]}

机构：

[1] State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 01期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Cloud manufacturing; Dynamic optimization; Machine tool; Markov jump system; Random disturbance;

D O I：

10.13196/j.cims.2022.01.004

中图分类号：

学科分类号：

摘要：

In cloud manufacturing (CMfg) environment, machine tools are susceptible to high-frequency random disturbances such as urgent order insertion, abnormal processing quality and equipment failures during the execution of manufacturing tasks, resulting in Quality of Service (QoS) cannot meet personalized demands from customers. To solve the problem, a discrete Markov jump system based dynamic matching method of machine tools was proposed. Based on the service operation characteristics in CMfg, a dynamic service quality evolution model toward the execution processing of manufacturing tasks was constructed. Combined with the system steady-state control theorem, a state feedback controller and a closed-loop control system were designed to describe the stability of cloud manufacturing task QoS. A dynamic optimization strategy of machine tools was proposed to mediate random disturbances during production. A simulation example proved that the proposed method could improve the comprehensive QoS of cloud manufacturing services by more than 10%, which verified the effectiveness and practicality of the method. © 2022, Editorial Department of CIMS. All right reserved.

引用

页码：43 / 50

页数：7

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