文献类型：会议论文

英文题名：The Application of GA-BP Algorithm in Prediction of Tool Wear State

作者：Tang, J.[1]; Li, W.X.[1]; Zhao, B.[2]

第一作者：唐军

机构：[1] Department of Mechanical and Electrical Engineering, Xinxiang University, Xinxiang, 453003, China; [2] School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 45400, China

第一机构：新乡学院机电工程学院

会议论文集：2nd International Conference on Manufacturing Technologies, ICMT 2018

会议日期：January 19, 2018 - January 21, 2018

会议地点：Orlando, FL, United states

语种：英文

外文关键词：Backpropagation - Forecasting - Genetic algorithms - Manufacture - Milling (machining) - Milling cutters - Neural networks - Productivity - Wear of materials

摘要：In CNC shaping milling machine, the prediction of the state of tool wear has important application significance to improve productivity, reduce scrap rate and avoid security risks. In this paper, the detection and control system of disk milling cutter is set up by the current monitoring method, the input characteristic quantity and target characteristic quantity of BP neural network for tool wear diagnosis are measured, and the disk milling cutter wear condition prediction neural network is established based on the GA-BP algorithm. At last, the online prediction of milling cutter wear state is realized. The network test results show that the prediction rate of tool wear condition is more than 92.78%. ? 2018 Institute of Physics Publishing. All rights reserved.