# A Novel Method for Tool Identification and Wear Condition Assessment Based on Multi-Sensor Data
## 1 Introduction  
HSC (high speed cutting) is one of the representatives of advanced manufacturing technology, which has the advantages of high precision, high speed, and high quality [1; 2]. In the milling process, the milling cutter directly contacts the workpiece, and the tool wear will directly cause the surface quality and machining accuracy of the workpiece to decline. Tool breakage will cause chatter, tool breakage and other phenomena, and even cause machine tool damage and production accidents [3; 4]. In order to improve the wear resistance of cutting tools, a lot of research has been undertaken [5; 6]. According to the statistical research, when the automatic production equipment is equipped with a tool state detection system, the downtime will be reduced by 75%, the utilization rate of machine tools will be increased by at least 50%, and the total production efficiency will be increased by 10%-60% [7]. Therefore, the real-time monitoring of tool wear is of great significance to improve product quality and reduce processing costs [8; 9; 10]. At present, many achievements have been made in the research of tool wear evaluation. In view of the massive data processing problem under the background of industry 4.0, [11] uses CNN (Convolutional Neural Networks) neural network algorithm to predict and classify the tool wear degree according to the collected cutting force, and proposes a relatively complete processing system--however, the overall evaluation accuracy is low. In the research on tool signal processing. [PERSON] combined tool wear with monitoring signal analysis based on modal analysis, and proposed a new tool wear estimation method, which used ensemble empirical mode decomposition (EEMD) to extract the signalcharacteristics of the processing process for real-time tool wear assessment [12]. [PERSON] put forward a method of measuring tool wear directly by using a visual system. This method uses Matrox software to process the gray image obtained by charge coupled device camera and to acquire the tool wear degree [13]. Driven by the monitored big data, [PERSON] systematically investigates the key issues for tool condition monitoring, such as machining dynamics, intelligent tool wear monitoring and compensation algorithms, heterogeneous big data fusion, and deep learning methods. Under this scheme, it develops the smart tool condition monitoring system that could improve the computer numerical control machining precision and productivity significantly [14]. In the optimization of tool wear condition monitoring system, [PERSON] combined derived wavelet frames (DWFs) and CNN. By decomposing the original signal into different bandwidth, different center frequency bands, the reconstructed sub signal is superposed into two-dimensional signal matrix, which matches the structure of two-dimensional CNN, and then the two-dimensional signal matrix is identified by neural network [15]. [PERSON] obtained the approximate linear relationship between the logarithm of cutting speed and the logarithm of milling distance based on the experiment of high-speed milling titanium alloy TC4 with cemented carbide tools and constructed the prediction model of tool wear [16]. However, the prediction of tool wear caused by cutting speed is only realized, and the prediction accuracy is too low. In the multi-disciplinary research of multi-sensor fusion technology, due to its versatility and wide application fields, it has attracted more and more interest, and it is also a worthy direction in the research of tool wear [17]. Therefore, in order to solve the problem of tool wear evaluation, this paper proposes a tool wear prediction model based on the multi-sensor Elman_Adaboost strong predictor. Currently AdaBoost is mainly used for classification [18; 19; 20]. In this paper, the function of AdaBoost is extended to predict.