Meta domain generalization for smart manufacturing: Tool wear prediction with small data

[1]  Yingguang Li,et al.  A Meta-Invariant Feature Space Method for Accurate Tool Wear Prediction Under Cross Conditions , 2022, IEEE Transactions on Industrial Informatics.

[2]  Rui Xie,et al.  Optimal transport-based transfer learning for smart manufacturing: Tool wear prediction using out-of-domain data , 2021, Manufacturing Letters.

[3]  Jipu Li,et al.  Deep Semisupervised Domain Generalization Network for Rotary Machinery Fault Diagnosis Under Variable Speed , 2020, IEEE Transactions on Instrumentation and Measurement.

[4]  Xu Li,et al.  Domain generalization in rotating machinery fault diagnostics using deep neural networks , 2020, Neurocomputing.

[5]  Minqiang Xu,et al.  Intelligent Fault Identification Based on Multisource Domain Generalization Towards Actual Diagnosis Scenario , 2020, IEEE Transactions on Industrial Electronics.

[6]  Dazhong Wu,et al.  Data-driven smart manufacturing: Tool wear monitoring with audio signals and machine learning , 2019, Journal of Manufacturing Processes.

[7]  Aryan Mokhtari,et al.  On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms , 2019, AISTATS.

[8]  Wennian Yu,et al.  Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme , 2019, Mechanical Systems and Signal Processing.

[9]  Marc Thomas,et al.  Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process , 2018, The International Journal of Advanced Manufacturing Technology.

[10]  Dazhong Wu,et al.  Deep learning for smart manufacturing: Methods and applications , 2018, Journal of Manufacturing Systems.

[11]  Connor Jennings,et al.  Cloud-Based Parallel Machine Learning for Tool Wear Prediction , 2018 .

[12]  Timothy M. Hospedales,et al.  Learning to Generalize: Meta-Learning for Domain Generalization , 2017, AAAI.

[13]  Connor Jennings,et al.  A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests , 2017 .

[14]  Chetan Gupta,et al.  Long Short-Term Memory Network for Remaining Useful Life estimation , 2017, 2017 IEEE International Conference on Prognostics and Health Management (ICPHM).

[15]  Dazhong Wu,et al.  A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing , 2017 .

[16]  Roshun Paurobally,et al.  A review of flank wear prediction methods for tool condition monitoring in a turning process , 2012, The International Journal of Advanced Manufacturing Technology.

[17]  Kai Cheng,et al.  Modeling flank wear of carbide tool insert in metal cutting , 2005 .

[18]  Joseph C. Chen,et al.  An artificial-neural-networks-based in-process tool wear prediction system in milling operations , 2005 .

[19]  Ricardo Vilalta,et al.  A Perspective View and Survey of Meta-Learning , 2002, Artificial Intelligence Review.

[20]  Li Dan,et al.  Tool wear and failure monitoring techniques for turning—A review , 1990 .