A novel adversarial domain adaptation transfer learning method for tool wear state prediction
Authors:
- Kai LiKai Li
- Mingsong ChenMCMingsong Chen
- Yongcheng LinYLYongcheng Lin
- Zhou LiZLZhou Li
- Xianshi JiaXJXianshi Jia
- Bin LiBLBin Li
- 10 August 2022
- journal article
- research article
- Published by Elsevier in Knowledge-Based Systems
- Vol. 254
Abstract
No abstract availableKeywords
This publication has 39 references indexed in Scilit:
- A generic tool wear model and its application to force modeling and wear monitoring in high speed millingMechanical Systems and Signal Processing, 2019
- Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in ManufacturingIEEE Transactions on Industrial Informatics, 2018
- Tool wear monitoring using an online, automatic and low cost system based on local textureMechanical Systems and Signal Processing, 2018
- Visual Domain Adaptation with Manifold Embedded Distribution Alignment2018
- In-process Tool Wear Prediction System Based on Machine Learning Techniques and Force AnalysisProcedia CIRP, 2018
- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network2017
- Adversarial Discriminative Domain Adaptation2017
- Machine vision micro-milling tool wear inspection by image reconstruction and light reflectancePrecision Engineering, 2016
- Multimodal Hidden Markov Model-Based Approach for Tool Wear MonitoringIEEE Transactions on Industrial Electronics, 2013
- Domain Adaptation via Transfer Component AnalysisIEEE Transactions on Neural Networks, 2010