An Optimization Method of Flexible Manufacturing System Reliability Allocation Based on Two Dimension-Reduction Strategies

IF 2.1 3区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Machines Pub Date : 2023-12-29 DOI:10.3390/machines12010024
Jingjing Xu, Long Tao, Yanhu Pei, Zhifeng Liu, Qiaobin Yan, Qiang Cheng
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Abstract

As increasingly extensive applications of flexible manufacturing systems (FMSs) arise, their reliability allocation has been a research hot spot. But, since FMSs are always composed of transfer and buffer devices, production machines, and complex control systems, the large number of basic elements makes the number of variables and constraints of reliability-allocation optimization increase greatly, which leads to the difficulty and inefficiency of optimization. To solve the above problem, two dimension-reduction strategies are proposed for the FMS reliability optimization with low cost and a high level of reliability as the objectives, and they are the reliability-weight double-threshold qualification strategy (RWTS) and the bi-level optimization strategy (BLOS), respectively. Based on these two strategies, an overall reliability-allocation optimization model and a bi-level reliability-allocation optimization model are established based on the FMS reliability evaluation presented in our previous work, and their algorithms based on particle swarm optimization (PSO) are presented. In terms of their contributions, for the RWTS, thresholds of reliability and the weight index of each basic element are set to dynamically reduce the number of variables in each iteration of the optimization; for the BLOS, the overall reliability-allocation optimization problem for transitioning from the FMS to basic elements can be transformed into simpler allocation optimizations from the FMS to subsystems and from subsystems to basic elements, which have fewer variables, and this can largely improve the optimization convergence performance. Through applying this to a box-parts finishing FMS, compared with the traditional optimization method, the high efficiency and the good allocation effect of the optimization based on these two strategies for improving convergence speed are verified by the simulation results. The proposed method has great significance for FMS design due to its limited cost but high-reliability requirement.
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基于两种降维策略的柔性制造系统可靠性分配优化方法
随着柔性制造系统(FMS)应用的日益广泛,其可靠性分配一直是研究热点。但是,由于柔性制造系统(FMS)总是由传送和缓冲装置、生产设备以及复杂的控制系统组成,大量的基本要素使得可靠性分配优化的变量和约束条件大大增加,从而导致优化的难度和效率降低。为解决上述问题,针对以低成本和高可靠性为目标的 FMS 可靠性优化,提出了两种降维策略,分别是可靠性权重双阈值限定策略(RWTS)和双级优化策略(BLOS)。在这两种策略的基础上,根据我们之前工作中提出的 FMS 可靠性评估,建立了整体可靠性分配优化模型和双级可靠性分配优化模型,并介绍了它们基于粒子群优化(PSO)的算法。就其贡献而言,对于 RWTS,通过设置可靠性阈值和各基本要素的权重指数,动态减少了每次优化迭代的变量数量;对于 BLOS,可将从 FMS 到基本要素过渡的整体可靠性分配优化问题转化为更简单的从 FMS 到子系统以及从子系统到基本要素的分配优化问题,变量数量更少,从而在很大程度上提高了优化的收敛性能。通过将其应用于箱形零件精加工 FMS,与传统的优化方法相比,基于这两种策略的优化在提高收敛速度方面的高效率和良好的分配效果得到了仿真结果的验证。由于 FMS 的成本有限,但可靠性要求高,因此所提出的方法对 FMS 的设计具有重要意义。
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来源期刊
Machines
Machines Multiple-
CiteScore
3.00
自引率
26.90%
发文量
1012
审稿时长
11 weeks
期刊介绍: Machines (ISSN 2075-1702) is an international, peer-reviewed journal on machinery and engineering. It publishes research articles, reviews, short communications and letters. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. Full experimental and/or methodical details must be provided. There are, in addition, unique features of this journal: *manuscripts regarding research proposals and research ideas will be particularly welcomed *electronic files or software regarding the full details of the calculation and experimental procedure - if unable to be published in a normal way - can be deposited as supplementary material Subject Areas: applications of automation, systems and control engineering, electronic engineering, mechanical engineering, computer engineering, mechatronics, robotics, industrial design, human-machine-interfaces, mechanical systems, machines and related components, machine vision, history of technology and industrial revolution, turbo machinery, machine diagnostics and prognostics (condition monitoring), machine design.
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