摘要
由于铣刀在高转速下进行不连续切削,刀具磨损迅速且难于监测,并且刀具磨损严重影响加工精度与产品质量。针对高速铣削刀具磨损难以在线预测问题,提出了一种基于深度学习的高速铣削刀具磨损预测的新方法。通过小波包变换提取铣削力信号在不同频段上的能量分布作为初始特征向量;采用无监督学习对稀疏自编码网络进行特征学习,并将单层网络堆栈构成深度神经网络;最后利用有监督学习对整个深度网络进行微调训练,建立铣削刀具磨损预测模型。实验结果表明,所提出的方法对刀具磨损状态预测准确率达到93.038%。
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%.
出处
《机械与电子》
2017年第7期12-17,共6页
Machinery & Electronics
关键词
高速铣削
刀具磨损
状态预测
深度学习
稀疏自编码
high-speed milling
tool wear
state prediction
deep learning
sparse auto-encoder
