材料工程中一种考虑鲁棒性的概率多目标优化方法

M. Zheng, H. Teng, Yi Wang
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摘要

简介/目的:基于概率的多目标优化(MOO)方法为了克服以往的多目标优化方法中主观因素和“可加性”因素的固有缺点,引入了优选概率的概念来表示优化中候选对象的优选程度。本文将该方法扩展到材料工程的鲁棒优化。以电动截止阀阀体熔炼过程的能量消耗为例,采用正交阵列设计和鲁棒优化四种不同工艺方案。方法:候选企业各性能效用指标的算术平均值对部分优选概率的贡献为一部分,而各性能效用指标与其算术平均值的偏差对部分优选概率的贡献为另一部分。在此基础上,根据新提出的基于概率的多目标优化(PMOO)方法,求出候选方案的总优选概率,从而将多目标优化问题转化为单目标优化问题。结果:在该炼钢过程中,捆扎钢、松散钢和未清洁钢的质量比分别为12.5%、50%和37.5%,是降低电能消耗且具有鲁棒性的最优控制因素。紧跟在这种情况之后的是50%捆绑钢、50%松散钢和0%未清洁钢的情况。方案一是对电动截止阀阀体加工的四种不同工艺方案进行鲁棒优化。结论:考虑鲁棒性的基于概率的多目标优化方法的推广是成功的,可以很容易地用于处理数据分散的优化问题,从而在材料工程中客观地获得具有鲁棒性的最优结果。在考虑鲁棒性的前提下,对基于概率的多目标优化进行扩展,有利于相关研究和过程优化。
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An approach of probability based multi-objective optimization considering robustness for material engineering
Introduction/purpose: The newly developed probability-based multi - objective optimization (MOO) has introduced a novel concept of preferable probability to represent a preferability degree of a candidate in optimization in order to overcome the inherent shortcomings of subjective and "additive" factors in the previous MOO methods. In this paper, the new method is extended to include robust optimization for material engineering. Furthermore, energy consumption in a melting process with orthogonal array design and the robust optimization of four different process schemes in machining an electric globe valve body are taken as examples. Methods: The arithmetic mean value of each performance utility indicator of the candidate contributes to one part of the partial preferable probability, while the deviation of each performance utility indicator from its arithmetic mean value of the candidate contributes to the other part of the partial preferable probability quantitatively. Furthermore, following the procedures of the newly developed probability-based multi-objective optimization (PMOO), the total preferable probability of a candidate is obtained, which thus transfers a multi-objective optimization problem into a single objective optimization problem. Results: The optimal control factors of lower electric energy consumption with robustness are bundled steel, loose steel, and uncleaned steel of 12.5%, 50% and 37.5% by weight, respectively, in this steel melting process. This case is closely followed by the scenario of 50 wt% bundled steel, 50 wt% loose steel, and 0 wt% uncleaned steel. The robust optimization of four different process schemes for machining an electric globe valve body is scheme No. 1. Conclusion: The extension of probability-based multi-objective optimization while considering robustness is successful, which can be easily used to deal with the optimal problem with dispersion of data to get objectively an optimal result with robustness in material engineering. The extension of probability-based multi-objective optimization while considering robustness will be beneficial to relevant research and process optimization.
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