基于神经网络的车削刀具磨损预测研究  

Prediction of Tool Wear Based on Neural Network

作　　者：杨丰[1] 李云[1] 

机构地区：[1]长沙航空职业技术学院,长沙410014

出　　处：《科技通报》2017年第12期118-120,157,共4页Bulletin of Science and Technology

基　　金：湖南省高等学校科学研究项目(14C0010)

摘　　要：针对切削难加工材料时刀具磨损对加工质量的影响,通过实验研究刀具磨损机理,在一定的切削参数条件下,利用神经网络预测刀具磨损规律,获得较好的磨损量预测精度,在此基础上提出了刀具磨损监测系统模型,为刀具加工补偿提供了理论依据。For the influence of product quality to cut hard-cutting material on tool wear, the tool wear mechanism was explored by experimental, neural network was proposed to realize the tool wear monitoring on given cutting parameters. The method not only improves precision, but also the practical model of tool wear monitoring is presents on the theories, provides a basic theory for tool wear compensation.

关 键 词：刀具磨损机理 神经网络 难加工材料 

分 类 号：TG711[金属学及工艺—刀具与模具]