scholarly journals Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 823 ◽  
Author(s):  
Alessandra Caggiano
Keyword(s):  
Pattern Recognition ◽  
Tool Wear ◽  
Wear Prediction ◽  
Tool Wear Prediction ◽  
Multiple Sensor ◽  
Multiple Sensor Monitoring ◽  
Sensor Monitoring

scholarly journals Multiple Sensor Monitoring for Tool Wear Forecast in Drilling of CFRP/CFRP Stacks with Traditional and Innovative Drill Bits

Procedia CIRP ◽  
2018 ◽  
Vol 67 ◽  
pp. 404-409 ◽  
Author(s):  
Alessandra Caggiano ◽  
Francesco Napolitano ◽  
Luigi Nele ◽  
Roberto Teti
Keyword(s):  
Tool Wear ◽  
Drill Bits ◽  
Multiple Sensor ◽  
Multiple Sensor Monitoring ◽  
Sensor Monitoring

scholarly journals Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Weixin Xu ◽  
Huihui Miao ◽  
Zhibin Zhao ◽  
Jinxin Liu ◽  
Chuang Sun ◽  
...  
Keyword(s):  
Tool Wear ◽  
Information Technologies ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Wear Prediction ◽  
Multi Scale ◽  
Tool Wear Prediction ◽  
Machine Health Monitoring ◽  
Gated Recurrent Unit ◽  
Unit Network

AbstractAs an integrated application of modern information technologies and artificial intelligence, Prognostic and Health Management (PHM) is important for machine health monitoring. Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry. In this paper, a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) is proposed to address raw sensory data for tool wear prediction. At the bottom of MCGRU, six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network, which augments the adaptability to features of different time scales. These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations. At the top of the MCGRU, a fully connected layer and a regression layer are built for cutting tool wear prediction. Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

Research on tool wear prediction based on temperature signals and deep learning

Wear ◽  
2021 ◽  
pp. 203902
Author(s):  
Zhaopeng He ◽  
Tielin Shi ◽  
Jianping Xuan ◽  
Tianxiang Li
Keyword(s):  
Deep Learning ◽  
Tool Wear ◽  
Wear Prediction ◽  
Tool Wear Prediction
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