云制造环境下基于离散Markov跳变系统的机床装备资源动态优选方法

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

针对云制造环境下加工任务执行过程中机床装备资源易受紧急插单、加工质量异常、设备运行故障等高频随机扰动影响,致使产品加工服务质量(QoS)不能满足客户个性化需求的问题,提出一种基于离散Markov跳变系统的机床装备资源动态优化选择方法。首先,结合云制造服务运行特点,构建了云制造环境下面向加工任务执行过程的服务质量动态演化模型;基于系统稳态控制定理,设计了云制造加工任务QoS的状态反馈控制器和闭环控制系统,并在此基础上提出面向生产随机扰动的机床装备资源动态优选策略;最后,通过应用实例仿真分析,所提方法将云制造服务的综合QoS值提升了10%以上,从而验证了该方法的有效性和实用性。

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.