切削参数的两阶段优化系统  

A Two-stage Optimization System for Cutting Parameters

作　　者：刘炳辉[1] 江小云[1] 

机构地区：[1]厦门理工学院管理学院,福建厦门361024

出　　处：《工业工程》2014年第6期30-35,共6页Industrial Engineering Journal

基　　金：厦门理工学院对外科技合作交流专项(DW12007)

摘　　要：以汽车轴承支架轴承孔的精镗切削为例,综合运用田口方法、灰色关联度分析、倒传递神经网络和粒子群算法等相关技术,对切削参数进行两阶段优化,迅速有效地找出最佳切削参数组合,不仅保证加工精度符合多目标要求,而且提升产品的稳定性。通过分析和验证表明,该方法实用有效,为提高机械加工企业的市场竞争力开辟了一种新的思路和途径。Based on the fine boring of the bearing holes of the auto bearing support, Taguchi experiments, grey relational analysis, the back-propagation neural network and particle swarm optimization are employed to optimize the cutting parameters. The two-stage optimization system can rapidly obtain the best cutting parameter settings to improve the quality of components and the stability of processing. The result demon- strates the feasibility and effectiveness of the proposed approach. It provides a novel approach and pathway for mechanical processing enterprises to enhance their competitiveness.

关 键 词：切削参数 田口方法 灰色关联度 倒传递神经网络 粒子群算法 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]