离子镀电池中机器灵活性的优化

F. Chan, K. Au, P. Chan
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引用次数: 0

摘要

在20世纪70年代,制造系统的性能严重依赖于生产率。制造商只专注于通过增加劳动力数量来提高生产率。自20世纪90年代以来,柔性的概念开始引入行业,制造商意识到柔性是通过响应制造系统中不断变化的环境来提高生产率的更好解决方案。然而,目前对离子镀机械柔性的研究还比较有限,大多集中在产品开发和镀层质量方面。本文的目的是确定离子镀电池(IPC)中机器灵活性的最佳水平,以提高整个系统的性能。提出了一种基于多目标遗传算法(GA)的机器装载排序(MLS)模型。在案例研究中,将某贵金属精加工企业的工业数据输入到所提出的机器装载排序遗传算法(MLSGA)模型中。在系统整体性能(即准时交货、产品质量和生产成本)最大化的情况下,将不同程度的机器灵活性分配给不同的机器,以确定最佳的机器。结果表明,在最近的IP技术下,IPC中的机器灵活性级别应该为零。但在提高涂布质量时,应引入机器柔性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of machine flexibility in an ion plating cell
In 1970s, manufacturing system performance was heavily depended on productivity. Manufacturers only concentrated on increasing productivity by increasing the number of workforce. The concept of flexibility began to introduce into the industry since 1990s, manufacturers realized that flexibility was a better solution to improve productivity by responding the changing environment in manufacturing system. However, limited researches on machine flexibility in ion plating (IP) industry were studied, most of them have focused on product development and quality of coating. The aim of this paper is to determine the optimal level of machine flexibility in an ion plating cell (IPC) to improve the entire system performance. A machine loading sequencing (MLS) model based on multi-objectives genetic algorithms (GA) was developed. In the case study, industrial data of a precious metal finishing company has been input into the proposed machine loading sequencing genetic algorithm (MLSGA) model. Different level of machine flexibility will be assigned into different machines to determine the optimum while the overall system performance (i.e. on-time delivery, quality of product and production cost) has been maximized. The results demonstrated that the machine flexibility level in IPC should be zero under the recent IP technology. However, when the quality of coating is improved, machine flexibility should be introduced.
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