Intelligent recognition of milling cutter wear state with cutting parameter independence based on deep learning of spindle current clutter signal

2020 ◽  
Vol 109 (3-4) ◽  
pp. 929-942
Author(s):  
Kaiyu Song ◽  
Min Wang ◽  
Liming Liu ◽  
Chen Wang ◽  
Tao Zan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Milling Cutter ◽  
Cutting Parameter ◽  
Cutter Wear ◽  
Parameter Independence ◽  
Intelligent Recognition ◽  
Spindle Current

scholarly journals Intelligent Recognition of Transmission Line Inspection Image Based on Deep Learning

2021 ◽  
Vol 1757 (1) ◽  
pp. 012056
Author(s):  
Yue Qi ◽  
Shile Mu ◽  
Jun Wang ◽  
Liangliang Wang
Keyword(s):  
Deep Learning ◽  
Transmission Line ◽  
Intelligent Recognition

A financial ticket image intelligent recognition system based on deep learning

2021 ◽  
pp. 106955
Author(s):  
Hanning Zhang ◽  
Qinghua Zheng ◽  
Bo Dong ◽  
Boqin Feng
Keyword(s):  
Deep Learning ◽  
Recognition System ◽  
Intelligent Recognition

scholarly journals Deep Learning-Based Classification of Weld Surface Defects

2019 ◽  
Vol 9 (16) ◽  
pp. 3312 ◽  
Author(s):  
Zhu ◽  
Ge ◽  
Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Surface Defects ◽  
Recognition Method ◽  
High Level ◽  
Intelligent Recognition ◽  
Non Destructive

In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the CNN. Random forest is trained with extracted high-level features to predict the classification results. Secondly, the weld surface defects images are collected and preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images. Finally, comparative experiments are performed on the weld surface defects database. The results show that the accuracy of the method combined with CNN and random forest can reach 0.9875, and it also demonstrates the method is effective and practical.

scholarly journals An Automatic Intelligent Diagnostic Mechanism for the Milling Cutter Wear

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 199359-199368
Author(s):  
Bo-Lin Jian ◽  
Kuan-Ting Yu ◽  
Xiao-Yi Su ◽  
Her-Terng Yau
Keyword(s):  
Milling Cutter ◽  
Cutter Wear
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