Adaptive modelling of the milling process and application of a neural network for tool wear monitoring

1996 ◽  
Vol 12 (1) ◽  
pp. 5-13 ◽  
Author(s):  
Tae Jo Ko ◽  
Dong Woo Cho
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Milling Process ◽  
Tool Wear Monitoring ◽  
Wear Monitoring ◽  
Adaptive Modelling

scholarly journals Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network

2021 ◽  
Vol 11 (24) ◽  
pp. 12041
Author(s):  
Qun Wang ◽  
Hengsheng Wang ◽  
Liwei Hou ◽  
Shouhua Yi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Tool Wear ◽  
Network Architecture ◽  
Manufacturing Systems ◽  
Convolution Neural Network ◽  
Tool Wear Monitoring ◽  
Monitoring Methods ◽  
Deep Convolutional Neural Networks ◽  
Wear Monitoring

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.

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