Designing the optimal process mean vector for mixed multiple quality characteristics
Designing the optimal process mean vector for mixed multiple quality characteristics
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DOI：
10.1080/0740817x.2012.655061
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发表时间：
2012-01
期刊：
影响因子：
--
通讯作者：
P. Goethals;B. Cho
中科院分区：
文献类型：
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作者：
P. Goethals;B. Cho
对于制造业来说，确定最优工艺平均值通常可以显著减少浪费并增加赚钱的机会。给定工艺规范限制和相关的返工或报废成本，确定最佳工艺平均值的传统方法包括在实施优化方案之前假定每个工艺分布参数的值。相反，本文建议将响应面方法整合到问题的框架中，从而消除了对参数进行假设的需要。此外，虽然研究人员已经研究了模型来研究单一质量特征和多个名义上最好的类型特征的研究问题，但本文专门研究了混合多质量特征问题。建立了考虑经济因素的非线性规划程序，便于最优过程均值向量的识别。文中还对成本结构、公差和质量损失设置对应的灵敏度进行了分析，以说明它们对解决方案的影响。
For the manufacturing community, determining the optimal process mean can often lead to a significant reduction in waste and increased opportunity for monetary gain. Given the process specification limits and associated rework or rejection costs, the traditional method for identifying the optimal process mean involves assuming values for each of the process distribution parameters prior to implementing an optimization scheme. In contrast, this article proposes integrating response surface methods into the framework of the problem, thus removing the need to make assumptions on the parameters. Furthermore, whereas researchers have studied models to investigate this research problem for a single quality characteristic and multiple nominal-the-best type characteristics, this article specifically examines the mixed multiple quality characteristic problem. A non-linear programming routine with economic considerations is established to facilitate the identification of the optimal process mean vector. An analysis of the sensitivity corresponding to the cost structure, tolerance, and quality loss settings is also provided to illustrate their effect on the solutions.