MULTI-OBJECTIVE OPTIMIZATION STRATEGY BASED ON ENTROPY WEIGHT, GREY CORRELATION THEORY, AND RESPONSE SURFACE METHOD IN TURNING
DOI:
https://doi.org/10.23055/ijietap.2021.28.5.7327Keywords:
Multi-Objective optimization strategy, Entropy, Grey correlation analysis, Response surface method, Machine tool specific energyAbstract
Machining with low energy consumption, high efficiency, and processing quality has become the preferred processing strategy for manufacturing enterprises. An effective multi-objective optimization method can help to formulate a good machining strategy. Gray correlation analysis is a powerful tool for formulating a multi-objective optimization strategy, and the parameters combination can be evaluated by the grey correlation degree. In multi-objective optimization problems, the weights assignment techniques are a key factor for making a decision. However, the conventional grey correlation analysis method ignores the weight of each optimization objective. Besides, the grey correlation analysis can only be carried out in the range of training samples, which will lead to inaccurate optimization results. Therefore, this paper develops a multi-objective optimization strategy based on entropy weight, grey correlation theory, and response surface method to improve the original defects and formulate the best machining strategy in turn. Firstly, in the orthogonal experiments of turning AISI 1045 steel with three factors and five levels, the machine tool-specific energy, surface roughness, and processing efficiency are taken as optimization objectives. Then the weight of optimization objectives is determined by entropy theory. Finally, the response surface method is introduced to establish the grey correlation degree prediction model according to the entropy results. Based on the particle swarm optimization algorithm, the optimum processing strategy is obtained without the limitation of training samples. The results illustrate that cutting depth is the most significant factor affecting grey correlation degree, and the grey correlation degree increases by 14.01% on the initial basis. The research establishes a mathematical model with grey correlation degree as the objective function, which provides a new idea for formulating a multi-objective optimization strategy.
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