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

机译:建模测试,诊断和返工操作,并在综合制造过程中优化其位置

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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。该方法包括一种功能测试操作的模型,其特征在于故障覆盖,假阳性和在测试中引入的缺陷,除了返工和诊断(诊断测试)操作以及具有变量成功率的操作以及自己的缺陷介绍机制。该模型可容纳产品实例上的多次返工尝试。该模型适用于框架内的框架内,以优化一般制造过程中测试/诊断/返工(TDR)操作的测试/诊断/返工(TDR)操作的位置和特性(故障覆盖/测试成本,Rework成功率/返工)。应用了一种新的搜索算法,被称为等待序列搜索(WSS)来遍历一般过程流程以执行产生的成本函数的累积计算。实际编码的遗传算法(RCGA)用于执行多目标优化,最小化产生的成本。通常复杂过程流程的示例用于展示算法的可行性。

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