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MODELING TEST, DIAGNOSIS, AND REWORK OPERATIONS AND OPTIMIZING THEIR LOCATION IN GENERAL MANUFACTURING PROCESSES

机译:在一般制造过程中对测试,诊断和返工操作进行建模并优化其位置

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摘要

This paper presents a test, diagnosis, and rework analysis model for use in manufacturing process modeling. The approach includes a model of functional test operations characterized by fault coverage, false positives, and defects introduced in test, in addition to rework and diagnosis (diagnostic test) operations that have variable success rates and their own defect introduction mechanisms. The model accommodates multiple rework attempts on a product instance. The model is applied within a framework for optimizing the location(s) and characteristics (fault coverage/test cost, rework success rate/rework cost) of Test/Diagnosis/Rework (TDR) operations in a general manufacturing process. A new search algorithm called Waiting Sequence Search (WSS) is applied to traverse a general process flow to perform the cumulative calculation of a yielded cost objective function. Real-Coded Genetic Algorithms (RCGAs) are used to perform a multi-objective optimization that minimizes yielded cost. An example of a general complex process flow is used to demonstrate the feasibility of the algorithm.
机译:本文介绍了用于制造过程建模的测试,诊断和返工分析模型。该方法包括功能测试操作模型,该模型以故障覆盖率,误报和测试中引入的缺陷为特征,此外返工和诊断(诊断测试)操作具有不同的成功率以及它们自己的缺陷引入机制。该模型可容纳对产品实例的多次重做尝试。该模型在框架内应用,以优化一般制造过程中测试/诊断/返工(TDR)操作的位置和特征(故障覆盖率/测试成本,返工成功率/返工成本)。一种称为等待序列搜索(WSS)的新搜索算法被用于遍历一般流程,以执行收益成本目标函数的累积计算。实编码遗传算法(RCGA)用于执行多目标优化,以最大程度地降低生产成本。一个通用的复杂处理流程的例子被用来证明该算法的可行性。

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