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.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference131 articles.
1. Toward Intelligent Machine Tool
2. Overview of Intelligent Manufacturing Equipment Industry;Zhang;Intell. Manuf.,2020
3. Special Plan for Scientific and Technological Innovation in the Field of Advanced Manufacturing Technology during the 13th Five-Year Plan Period (Selected);China Metrol.,2017
4. A review of flank wear prediction methods for tool condition monitoring in a turning process
5. Digital Twin in Industry: State-of-the-Art;Fei;IEEE Trans. Ind. Inform.,2019
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