Optimization of Milling Process Parameters Based on Real Coded Self-adaptive Genetic Algorithm and Grey Relation Analysis

被引：2
|
作者：
Zeng, Shasha [1 ]
Yuan, Lei [2 ]
机构：
[1] Wuhan Univ, Sch Power & Mech Engn, Hubei Key Lab Waterjet Theory & New Technol, Wuhan 430072, Peoples R China
[2] Hainan Univ, Mech & Elect Engn Coll, Haikou 570228, Hainan, Peoples R China
来源：
INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2017, PT III | 2017年 / 10464卷
基金：
中国国家自然科学基金;
关键词：
Process parameter optimization; Real coded self-adaptive genetic algorithm; Grey relational analysis; Surface topography; SURFACE-ROUGHNESS; DESIGN;
D O I：
10.1007/978-3-319-65298-6_77
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a method to optimize the milling process parameters based on the real-coded self-adaptive genetic algorithm (RAGA) and Grey relational analysis (GRA) is proposed. Experiments have been designed with four input milling process parameters at four different levels. The RAGA coupled with GRA has been applied for solving the proposed optimization problem to achieve the desired machined surface quality characteristics. Simulation experiments give the optimal parametric combination. Furthermore, experiments for the machined surface topography with the initial and optimal combination of milling process parameters are implemented and the results verify the feasibility of the proposed method.
引用
收藏
页码：867 / 876
页数：10
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