Abstract: The advance of digital twin technology has resulted in a data enriched environment,making it possible that manufacturers can trace the various disruption events of machine breakdown and quality failure in the production processes.To scientifically evaluate and promptly response to the impact of disruption events on the system performance,scientifically evaluate the system performance and promptly response to production disruptions,the serial production lines with quality scrap was focused to study the performance evaluation and optimal allocation of quality inspection machines.Quality scrap events were defined based on real quality inspection operations.An event-based model of system permanent production loss was established to quantitatively analyze the impact of machine breakdown and quality failure events on productivity.Based on the event-based model,an optimization method was proposed to optimize the allocation of quality inspection machines,and the overall system cost including the cost of permanent production loss and quality inspection cost was minimized.A genetic-based algorithm was utilized to solve the problem and the optimal allocation of quality inspection machines was obtained.According to the simulation studies,the proposed method reduced the quality inspection cost while maintaining the satisfied level of system throughput.

Key words: digital twin, serial production line, quality scrap, event-based modeling, allocation of quality inspection machines

摘要： 数字孪生车间环境下获取生产数据更加便捷,使系统运行过程中收集机器故障、质检报废等扰动事件成为可能。为了科学评估并及时应对扰动事件对系统性能的影响,面向考虑质检报废的流水线,研究流水线性能评估与质检机器配置优化问题。考虑流水线中的质量检测操作,定义了质检报废事件,定量分析了机器故障与质检报废等扰动事件对系统产出的影响,建立了事件驱动的系统生产损失模型;在此基础上,综合考虑系统生产损失成本与质检机器投入成本,以最小化系统总成本为目标,建立了质检机器配置优化模型,并采用遗传算法求解获得了质检机器配置优化方案。通过仿真实验表明,所提优化方法既可保证系统产出,又可降低质量检测成本。

关键词: 数字孪生, 流水线, 质检报废, 事件驱动建模, 质检机器配置