# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
###### Abstract  
Tool wear monitoring is of great significance for the development of manufacturing systems and intelligent manufacturing. Online tool condition monitoring is a crucial technology for cost reduction, quality improvement, and manufacturing intelligence in modern manufacturing. However, it remains a difficult problem to monitor the status of tools online, in real-time and accurately in the industry. In the research status of mainstream technology, the convolution neural network may be a good solution to this problem, based on the appropriate sensor system and correct signal processing methods. Therefore, this paper outlines the state-of-the-art systems encountered in the open access literature, focusing on information collection, feature selection-extraction technologies based on deep convolutional neural networks, and monitoring network architecture and modeling methods. Based on typical cases, this paper focuses on the application of the convolution neural network in tool wear monitoring. From the application results, it is feasible and reliable to apply convolution neural networks in tool wear monitoring. Additionally, it can improve the prediction accuracy, which is of great significance for the future development of technology. This paper can be a guide for the researchers and manufacturers in the area of tool wear monitoring for explaining the latest trends and requirements. tool wear; convolutional neural network; network structure; tool condition monitoring 2021  
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tear significantly increase machining costs and maintenance time, reducing production efficiency. Tool condition monitoring employs effective detection methods, feature analysis, and pattern recognition to efficiently and accurately monitor the tool wear condition during the machining process. It provides a basis for subsequent control and processing, thereby reducing the manufacturing losses caused by the tool wear [4]. Effective tool wear monitoring can improve tool utilization, machining accuracy and production efficiency, increase the service life of machine tools, and ensure safe equipment operation, etc. At present, the importance of digital twin technology is increasingly recognized by academia and the industry, and it is recognized as one of the most promising technologies to realize intelligent manufacturing and Industry 4.0 [5; 6; 7; 8]. Its purpose is to create a digital object that reflects the actual object behavior, and this technology can be successfully applied to prediction, decision making, and reconstruction [9]. Using neural networks to extract and fuse features of multi-source data in the machining process, and then to monitor tool wear and predict tool life can promote the development of tool digital twin technology [10; 11; 12]. The tool wear refers to the deformation or damage of the sharp edge caused by the interaction between the tool and the workpiece during machining. It is a complicated process for monitoring the tool wear condition in order to realize the self-sensing and self-decision in the machining process. A system is nonlinear if its output is not proportional to its input. The inputs of the tool wear monitoring system, such as cutting force, vibration, stress elastic wave, temperature, current, power and workpiece surface quality, are not proportional to wear. Due to the coupling of working conditions, cutting parameters, workpiece material, blade deviation, cutting vibration and other factors, it is difficult to accurately model and predict wear in the cutting process. Tool wear is a very complex nonlinear system [11]. Traditional, supervised learning algorithms formulated various recognition and prediction models for the wear and damage of cutting tools, which achieved recognition outcomes.