深度学习与多信号融合在铣刀磨损状态识别中的研究

Study on Wear State Recognition of Milling Cutter via Deep Learning and Multi-signal Fusion

为精确地识别刀具磨损状态,提出了一种深度学习与多信号融合相结合的识别方法。以自编码网络为基础,构建了堆叠稀疏自编码网络。采集铣刀不同磨损状态下的力信号、振动信号及声发射信号,并对上述信号进行小波包分解以便获取能够表征铣刀磨损的时频域特征。利用无监督学习和有监督学习对堆叠稀疏自编码网络进行训练,建立了深度学习的铣刀磨损状态识别模型。研究结果表明,多信号融合的深度学习模型对铣刀磨损状态识别准确率达到94.44%。

The cutting tool is one of the most active factors in the machining,its status directly affects the surface quality of the workpiece.To accurately identify tool wear,a recognition method combining deep learning with multi-signal fusion was proposed.Based on the autoencoder network,the stacked sparse autoencoder network was constructed.The force signal,vibration signal and acoustic emission signal were collected under different wear condition of milling cutter,and the wavelet packet decomposition was carried out to obtain the time-frequency characteristics of milling cutter wear.The stacked sparse autoencoder network was trained by using the unsupervised learning and supervised learning,a recognition model for wear state of milling tool based on the deep learning was established.The results show that the deep learning model combing with the multi-signal fusion has an accuracy rate of 94.44%for identifying the wear state of milling cutter,the results lay a foundation for controlling the optimization of milling process.