# Tool Wear Monitoring for Complex Part Milling Based on Deep Learning
## 1 Introduction
[18] used SVM with the features extracted from discrete wavelet transformation in sound signals for tool condition monitoring in face milling. [PERSON] et al. [19] used support vector regression (SVR) with the features extracted from multi-sensor signals to predict tool life. [PERSON] and [PERSON] [20] used a least-square SVM to develop a nonlinear regression model for tool wear prediction in the milling process. [PERSON] et al. [21] proposed a weighted hidden Markov model for tool life prediction. [PERSON] and [PERSON] [22] proposed a hidden semi-Markov model with dependent durations through cutting force signals for tool wear monitoring. [PERSON] et al. [23] established a dynamic Bayesian network for tool condition monitoring and remaining useful life estimation. [PERSON] et al. [24] proposed a Gaussian process regression model for tool wear prediction. [PERSON] et al. [25] made a comparative study on different machining learning method including ANN, SVR and random forest for tool wear monitoring. [PERSON] et al. [26] built an Elman_Adaboost predictor with Elman neural networks for milling tool wear assessment with several statistic features selected from multi-sensor data including spindle current, force, vibration and acoustic emission. The accuracy and generalization ability of the above-mentioned conventional feature-based machining learning models are highly affected by the quality of the hand-designed features [27]. However, feature selection is problem-dependent and somewhat subjective in practice. Different from feature-based machining learning methods, deep learning can achieve adaptive feature learning, which is helpful to improve the adaptability of prediction methods. Moreover, layer-by-layer feature learning in deep network is more likely to learn essential features hidden in the monitoring data and then to improve prediction accuracy [28]. Common deep learning methods include deep multi-layer perceptron (DMLP), deep autoencoder (DAE), convolutional neural network (CNN) and long short-term memory (LSTM) network. [PERSON] et al. [29] used DMLP neural networks to predict surface roughness and specific energy consumption during 5-axis milling. [PERSON] et al. [30] proposed an online sequential extreme learning machine for tool wear state recognition with a stacked denoising autoencoder (SDAE) put forward to extract abstract features. [PERSON] et al. [31] proposed a 2-D CNN for milling tool wear monitoring, with the spectrum of the high signal-to-noise ratio vibration signals obtained from the derived wavelet frames as input features. [PERSON] et al. [32] employed a CNN with a hybrid feature extraction method using wavelet time-frequency transformation and spectral subtraction algorithms for tool wear estimation. [PERSON] et al. [33] built a CNN model with time series imaging technique to transform the input raw signals. [PERSON] et al. [34] designed an LSTM network to predict multiple flank wear values using raw signals of cutting force, vibration and acoustic emission. [PERSON] et al. [35] used convolutional bi-directional LSTM networks for tool condition monitoring in milling process. From the above, most existing deep learning methods use raw signals without feature pre-selection and hand-design to maximize the merits of deep learning. In the actual machining process, cutting conditions are strongly time-varying, especially for the milling of parts with complex geometry, of which the allowance and cutting depth drastically change along the whole tool path [36]. In most cases, in the actual machining environment, only a few types of monitoring signals are possible to acquire, and the input features from the signals are probably highly correlated, leading to low prediction accuracy and poor generalization ability in time-varying cutting conditions. Besides, the model based on the raw signal as input has weak interpretability and it is difficult to analyze the source of error. To solve this problem, a tool wear monitoring method for complex part milling based on deep learning is proposed in this paper.