Semi-supervised graph convolutional network to predict position- and speed-dependent tool tip dynamics with limited labeled data
Chaochao Qiu, Ling Yin(Dongguan University of Technology), Songping He(Huazhong University of Science and Technology), Bin Li(Huazhong University of Science and Technology), Kai Li(Central South University), Caihua Hao(Huazhong University of Science and Technology), Xinyong Mao(Huazhong University of Science and Technology)
Cited by 29
Related Papers
Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification
|IEEE Transactions on Industrial Electronics|2018|303
Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprecision Manufacturing
|IEEE Transactions on Industrial Electronics|2018|139
Tool Wear Prediction via Multidimensional Stacked Sparse Autoencoders With Feature Fusion
|IEEE Transactions on Industrial Informatics|2019|104
A novel adversarial domain adaptation transfer learning method for tool wear state prediction
|Knowledge-Based Systems|2022|66
Bayesian uncertainty quantification and propagation for prediction of milling stability lobe
|Mechanical Systems and Signal Processing|2019|56