Multi-objective Optimization Decision of High-speed Dry Hobbing Process Parameters Based on GABP and Improved NSGA-Ⅱ

被引：3
|
作者：
Liu Y. [1 ]
Yan C. [1 ]
Ni H. [1 ]
Mou Y. [1 ]
机构：
[1] State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing
来源：
Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2021年 / 32卷 / 09期
关键词：
Genetic algorithm-back propagation(GABP) neural network; High speed dry cutting; Hobbing process parameter; Improved non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ); Maximum tool life; Minimum machining energy consumption;
D O I：
10.3969/j.issn.1004-132X.2021.09.005
中图分类号：
学科分类号：
摘要：
A method was proposed to optimize and decide parameters in the situation of high speed dry hobbing, supported by example prediction and multi-objective genetic optimization algorithm. Based on actual processing sample sets, the subject model was a variant of NSGA-Ⅱ, the optimization goal was the maximum tool life and the minimum energy consumption. The improved GABP neural network was used to construct a prediction model for the processing optimization fitness function, and a similar instance set of the hobbing problem was obtained through the DBSCAN clustering algorithm, so as to establish multi-objective optimization constraints, and construct the multi-objective optimization model for optimizing and deciding process, which may search the optimal processing parameters iteratively. © 2021, China Mechanical Engineering Magazine Office. All right reserved.
引用
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页码：1043 / 1050
页数：7
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