Abstract In a multistage serial production line, multiple inspection stations and repair processes are typically involved to ensure high product quality. Quality rework is the activity to repair or repeat the work on the defect parts during manufacturing processes. The rework process after each inspection can add cost and cycle time to the normal process and impose negative impact on the throughput. This paper studies real-time performance of multistage serial manufacturing systems with quality rework loops and machine random failures. A production line with multiple quality rework loops is first unify by segmenting it into a set of serially connected quality rework loops. An event-based data-enabled mathematical model is developed to evaluate real-time production rate of each machine for such a system. In addition, the system properties are analyzed and permanent production loss due to quality rework loops and random machine failures are identified respectively. The permanent production loss attribution to each disruption event and machine can be used as real-time performance indicators to diagnose production system inefficiency. The mathematical model and system performance identification methodology are studied analytically and validated through numerical case studies.

中文翻译：

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具有质量返工循环的多阶段制造系统的数据支持建模和分析

摘要 在多级串行生产线中，通常会涉及多个检测站和维修过程，以确保产品的高品质。质量返工是在制造过程中对缺陷零件进行修复或重复工作的活动。每次检查后的返工过程会增加正常过程的成本和周期时间，并对吞吐量产生负面影响。本文研究了具有质量返工循环和机器随机故障的多级串行制造系统的实时性能。具有多个质量返工循环的生产线首先通过将其分割为一组串联的质量返工循环来统一。开发了一种基于事件的数据启用数学模型，以评估此类系统中每台机器的实时生产率。此外，对系统特性进行分析，并分别识别由于质量返工循环和随机机器故障造成的永久性生产损失。每个中断事件和机器的永久性生产损失归因可用作实时性能指标来诊断生产系统效率低下。数学模型和系统性能识别方法通过数值案例研究进行分析和验证。