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Wavelet-based hybrid CNN-BiLSTM approach in tool wear monitoring
Ahmed Abdeltawab
, Zhang Xi
, Zhang Longjia
,
Ahmed M. Galal
Mechanical Engineering
Shanghai University
Mansoura University
Research output
:
Contribution to journal
›
Article
›
peer-review
1
Scopus citations
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Dive into the research topics of 'Wavelet-based hybrid CNN-BiLSTM approach in tool wear monitoring'. Together they form a unique fingerprint.
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Engineering
BiLSTM
100%
Convolutional Neural Network
100%
Critical Role
20%
Deep Learning Method
100%
Energy Distribution
20%
Error Coefficient
20%
Frequency Component
20%
Learning Technique
40%
Manufacturing Process
20%
Mean Absolute Error
40%
Metrics
20%
Progression
20%
R-Value
20%
Regression Coefficient
20%
Root Mean Square Error
40%
Sensor Data
20%
Significant Change
20%
Square Regression
20%
Computer Science
Bidirectional Long Short-Term Memory Network
100%
Convolutional Neural Network
100%
Deep Learning Method
40%
Deep Learning Model
20%
Deep Learning Technique
40%
Energy Distribution
20%
Frequency Component
20%
Mean Absolute Error
40%
Multiresolution Analysis
20%
Performance Metric
20%
Regression Coefficient
20%
Wavelet Transform
100%
Chemical Engineering
Deep Learning Method
100%
Material Science
Milling
100%