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Hybrid multiobjective optimization algorithm based on multivariate mean square error and fuzzy decision maker
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DOI：10.1016/j.asoc.2019.105586.png)
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Abstract
En 中文
This work presents the turning process of AISI H13 hardened steel with the PCBN 7025 tool, considering six output variables: tool life, machining total cost, surface roughness, machining force, sound pressure level, and specific cutting energy. Several problems are encountered in engineering processes that have adverse effects on the reliability of complex engineering systems. Hence, the aim of this work is to optimize the hardened steel turning process by applying mathematical methods to reduce dimensionality and eliminate the correlation between the multiple responses. The resultant latent response surfaces and their respective targets constitute the normalized multivariate mean square error (MMSE) function that is minimized by the normal boundary intersection (NBI) method. Furthermore, a fuzzy algorithm is applied to identify the best solution from several feasible solutions of the Pareto frontier that is compared with the performances of normalized normal constraint, arc homotopy length, global criterion method, and desirability method. The results show that NBI-MMSE has a higher performance than the other methods. In addition, NBI-MMSE is tested with benchmark functions to evaluate its effectiveness and robustness. Therefore, NBI-MMSE identifies the dynamics of the turning process of AISI H13 steel by revealing the optimal solutions for the input process parameters. (C) 2019 Published by Elsevier B.V.
Keywords:
Principal component analysis
Normal boundary intersection
Multivariate mean square error
Fuzzy decision maker
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