# Tool Wear Monitoring for Complex Part Milling Based on Deep Learning
## 1 Introduction  
Tool wear is a cost driver in machining that affects quality and productivity and adds unscheduled downtime for tool changes and the reworking of damaged parts. Accurate tool wear monitoring is necessary to avoid these unnecessary costs. Tool wear monitoring methods can be categorized into two main groups: direct and indirect methods [1]. Direct methods measure the actual wear value with optical, laser or ultrasonic devices. Although direct methods measure tool wear precisely, they are difficult to implement in real-time machining because, in most cases, the tool wear area is unreachable due to the occlusion of workpiece structure and flood coolant. Indirect methods monitor in-process physical parameters to evaluate wear state, such as force, vibration, acoustic emission, current, power and temperature signals [2]. Nowadays, indirect methods are the most widely used in tool wear monitoring because they are easy to conduct in real time and can obtain acceptable accuracy by using a proper monitoring signals and modeling method. Indirect tool wear monitoring methods can be divided into two categories: physical-based and data-driven methods. Physical-based methods first develop the hand-designed physical model,i.e., the mathematic relationship between tool wear and measurable physical quantities from the mechanism of machining, and then estimate tool wear value through monitoring signals based on the model. [PERSON] and [PERSON] [3] proposed an evaluating approach of milling tool flank wear based on the relationship between average tangential cutting force coefficients and tool wear. [PERSON] [4] discussed the influences of process parameters, tool parameters, and tool wear on tangential cutting force coefficients in his dissertation. A recognition approach for milling tool wear was proposed, based on the relationship between tangential cutting force coefficients and tool wear. This approach was needed to solve the cutting force coefficients through actual cutting forces, and then the tool wear was recognized through the solved tangential cutting force coefficients. The complex calculations meant that the recognition speed was low, and the result was not precise enough. [PERSON] et al. [5] established a cutting power model in face milling, which included the cutting conditions and the tool flank wear, and then proposed a tool wear monitoring approach based on this power model. [PERSON] et al. [6] developed the relationship between flank wear and average milling force based on the stress distribution in the tool wear zone and applied this relationship to estimate the flank wear width in the milling process. [PERSON] et al. [7; 8] proposed a mechanistic cutting force model considering various wear types for difficult-to-cut material drilling, which can be used for tool wear monitoring and process parameter optimization in the conditions of multiple tool wear existing on different cutting edges. Due to the complexity of tool wear mechanism and randomness of the machining process, the physical-based monitoring methods are faced with the problem of low accuracy and weak universality [9; 10]. In recent years, many data-driven tool wear monitoring methods have been proposed based on machine learning such as fuzzy logic, artificial neural network (ANN), support vector machine (SVM), and Bayesian networks. [PERSON] [11] used logistic regression with penalization and manifold regularization for tool condition monitoring. [PERSON] et al. [12] used ANN with the inputs of cutting speed, feed rate and depth of cut for tool wear prediction in the milling of Ti-6242S. [PERSON] et al. [13] used ANN with thrust force signals for tool wear prediction in micro-drilling. [PERSON] et al. [14] built an ANN-based cognitive decision-making system to extract signal features for online tool life prediction. In other studies, the ANNs with different inputs and architectures are employed to predict tool wear [15; 16; 17]. [PERSON] et al.