Tool wear monitoring of TC4 titanium alloy milling process based on multi-channel signal and time-dependent properties by using deep learning

Abstract

Tool wear in machining of TC4 can largely affect processing efficiency and quality, and unaware of tool condition may cause huge economic losses. This paper presents a monitoring method of tool wear, which can predict the tool wear in real time from the force and acceleration signals that acquired during the milling process. Firstly, the time domain signal is transformed into two-dimensional time-frequency domain signal. Afterwards, the two-dimensional signal is concatenate with the number of cuts after dimension-increment, and then the experimental data and labels are put into the residual network for training. The mean square error (MSE) of real wear and predicted wear is taken as loss function. Comparing with the process of feature extraction, it is found that the prediction of deep learning is time-saving, and it can be used for tool wear prediction with maximum error around 8 μm. Finally, various length and combination signals are input into the trained residual network to test the generalization and transferability of network.