面向核心机床制造生产线瓶颈分析的信息物理系统建模

Jerahmeel K. Coching, Adrian Jenssen L. Pe, Seth Gabriel D. Yeung, W. Akeboshi, R. Billones
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引用次数: 0

摘要

制造企业一直面临着运营方面的挑战,即确保现有的制造流程和系统能够在最大限度地减少资源使用的同时,提供高质量产品的预期生产率。这适用于位于印度泰米尔顿岛哥印拜陀的Sakthi汽车零部件有限公司(SACL)核心车间,其目前的核心车间制造生产基础设施由11台不同的机器组成,负责执行11个步骤的制造过程。已经进行了几个案例研究,以研究改善该系统的可能干预措施。尽管采用了加工工艺修改,但系统评价显示,制造生产线仍然缺乏,无法满足平均每月24300辆的核心需求。瓶颈分析是一种标准的制造和生产管理方法,用于评估和增加与利用率和效率指标相关的系统能力。本文说明了网络物理系统(CPS)提供的一个机会,通过使用MATLAB SIMULINK对SACL的制造生产基础结构进行建模,自动验证基于瓶颈分析的迭代实现的改进。经过不断迭代的瓶颈分析,最终的模型配置可以在27个工作日内满足24300个核的预期需求,每天总共工作8个小时,只有7个小时是有效的。仿真结果表明,系统利用率为87.47%,系统效率为99.96%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cyber-Physical System Modeling for Bottleneck Analysis of the Manufacturing Production Line of Core Machines
Manufacturing firms are constantly faced with operational challenges of ensuring that their existing manufacturing processes and systems can deliver expected production rates of high-quality products while minimizing the extensive use of resources. This holds for the Sakthi Auto Component Ltd. (SACL) core shop based in Coimbatore, TN, India, whose current core shop manufacturing production infrastructure consists of eleven distinct machines responsible for carrying out an eleven-step manufacturing process. Several case studies have been conducted to research possible interventions to improve the system. Despite adopting machining process modifications, system evaluations show that the manufacturing production line is still considered lacking and unable to meet the average 24300 monthly core demand. Bottleneck analysis is a standard manufacturing and production management approach to evaluate and increase system capacities relative to utilization and efficiency metrics. This paper illustrates an opportunity presented by Cyber-Physical systems (CPS) to automate the validation of improvements that are based on iterative implementations of bottleneck analysis by modelling the manufacturing production infrastructure of SACL using MATLAB SIMULINK. Following successive iterations of bottleneck analysis, the final model configuration could meet the expected demand of 24300 cores within 27 working days, with each day having a total of 8 work hours, and only 7 hours being productive. The simulation reported an 87.47% system utilization rate and a 99.96% system efficiency rate.
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