机械与电子2017，Vol.35Issue(7)：12-17,6.
基于深度学习的高速铣削刀具磨损状态预测方法
A Deep Learning-Based Method for Tool Wear State Prediction in High Speed Milling
林杨 1高思煜 2刘同舜 1朱锟鹏2
作者信息
- 1. 中国科学技术大学 信息科学与技术学院自动化系,安徽 合肥 230026
- 2. 中国科学院 合肥物质科学研究院先进制造技术研究所,江苏 常州 213164
- 折叠
摘要
Abstract
Due to discontinuous cutting of the milling cutters operating at high rotational speed,the milling tools wear quickly with difficult monitoring,which seriously affect the machining precision and product quality.In order to solve the on-line prediction problems of high speed milling tool wear,a deep learning-based method for predicting tool wear state in high speed milling is proposed in this paper.Firstly,a wavelet based method was used to extract the energy distribution of cutting force at different frequency bands as the initial feature vectors;Secondly,an unsupervised learning method was employed to learn the features of the sparse auto-encoder network,and the single layer networks were stacked to construct the deep neural network;Finally,the whole deep learning network was fine-tuned by a supervised learning method,and the prediction model of tool wear state was established.The experimental results show that the prediction accuracy of the proposed method is 93.038%.关键词
高速铣削/刀具磨损/状态预测/深度学习/稀疏自编码Key words
high-speed milling/tool wear/state prediction/deep learning/sparse auto-encoder分类
信息技术与安全科学引用本文复制引用
林杨,高思煜,刘同舜,朱锟鹏..基于深度学习的高速铣削刀具磨损状态预测方法[J].机械与电子,2017,35(7):12-17,6.
