A multi-objective optimization model for process targeting with inspection errors using 100 % inspection

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• 作者：S. O. Duffuaa…
• 关键词：Process modeling ; Multi ; objectives ; Inspection systems ; Measurement errors
• 刊名：The International Journal of Advanced Manufacturing Technology
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：88
• 期：9-12
• 页码：2679-2692
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design;
• 出版者：Springer London
• ISSN：1433-3015
• 卷排序：88

文摘

In this paper, the problem of process targeting is considered in a situation where several objectives are sought, the product quality is controlled using 100 % inspection and the inspection system is error prone. The model extends the work of the literature of Duffuaa and El-Ga’aly (Appl Math Model 37(3):1545–1552, 2013a) by incorporating measurement errors in the inspection system. The quality characteristic under consideration is normally distributed with two market specification limits. The product satisfies the first specification limit which is sold in a primary market at a regular price, and products failing the first specification limit and satisfying the second one is sold in a secondary market at a reduced price. The product is reworked if it does not satisfy both specification limits. The multi-objective optimization model consists three objective functions, which are to maximize profit, income, and product uniformity using the Taguchi quadratic function as a surrogate for product uniformity. The concept of cutoff points (the decision during inspection is based on these cutoff points rather than specification limits) is used to counter and reduce the impact of inspection errors. An algorithm is proposed to obtain and rank the set of Pareto-optimal points. An illustrative numerical example and an industrial case study are presented to demonstrate the utility of the model. A sensitivity analysis is conducted to study the effect of the error on the optimal process mean and cutoff points.