An effective cellular particle swarm optimization for parameters optimization of a multi-pass milling process

Authors:
Liang Gao;Jida Huang;Xinyu Li
Affiliations:
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Venue:
Applied Soft Computing
Year:
2012

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Abstract

In the milling process, the selection of machining parameters is very important as these parameters determine the processing time, quality, cost and so on, especially in the high-accuracy machine tools. However, the parameters optimization of a multi-pass milling process is a nonlinear constrained optimization problem which is difficult to be solved by the traditional optimization techniques. Therefore, in order to solve this problem effectively, this paper proposes a novel parameters optimization method based on the cellular particle swarm optimization (CPSO). To address the constraints efficiently, the proposed method combines two constraints handling techniques, including the penalty function method and the constraints handling strategy of PSO. In the proposed CPSO, the smart cell constructs its neighborhood with self-adaptive function and constraints handling techniques, which guide the unfeasible particles to move to the feasible regions and search for better solutions. A case is adopted and solved to illustrate the effectiveness of the proposed CPSO algorithm. The results of the experiment study are analyzed and compared with those of the previous algorithms. The experimental results show that the proposed approach outperforms other algorithms and has achieved significant improvement.