标题
Physics-informed meta learning for machining tool wear prediction
作者
关键词
Physics-informed neural networks, Meta-learning, Tool wear prediction, Smart manufacturing
出版物
JOURNAL OF MANUFACTURING SYSTEMS
Volume 62, Issue -, Pages 17-27
出版商
Elsevier BV
发表日期
2021-11-11
DOI
10.1016/j.jmsy.2021.10.013
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Physics-guided logistic classification for tool life modeling and process parameter optimization in machining
- (2021) Jaydeep Karandikar et al. JOURNAL OF MANUFACTURING SYSTEMS
- Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling
- (2021) Mohsen Marani et al. MEASUREMENT
- Metric-based meta-learning model for few-shot fault diagnosis under multiple limited data conditions
- (2021) Duo Wang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Data Construction Method for the Applications of Workshop Digital Twin System
- (2020) Tianxiang Kong et al. JOURNAL OF MANUFACTURING SYSTEMS
- A personalized diagnosis method to detect faults in gears using numerical simulation and extreme learning machine
- (2020) Xiaoyang Liu et al. KNOWLEDGE-BASED SYSTEMS
- Physics-informed neural networks for missing physics estimation in cumulative damage models: a case study in corrosion fatigue
- (2020) Arinan Dourado et al. JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING
- Physics-informed neural networks for inverse problems in nano-optics and metamaterials
- (2020) Yuyao Chen et al. OPTICS EXPRESS
- Digital twin for cutting tool: Modeling, application and service strategy
- (2020) Yang Xie et al. JOURNAL OF MANUFACTURING SYSTEMS
- Physics guided neural network for machining tool wear prediction
- (2020) Jinjiang Wang et al. JOURNAL OF MANUFACTURING SYSTEMS
- An accurate prediction method of multiple deterioration forms of tool based on multitask learning with low rank tensor constraint
- (2020) Changqing Liu et al. JOURNAL OF MANUFACTURING SYSTEMS
- A Comprehensive Survey on Transfer Learning
- (2020) Fuzhen Zhuang et al. PROCEEDINGS OF THE IEEE
- CEEMD-assisted bearing degradation assessment using tight clustering
- (2019) Yanfei Lu et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Tool wear classification using time series imaging and deep learning
- (2019) Giovanna Martínez-Arellano et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks
- (2018) Rui Zhao et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- A generic tool wear model and its application to force modeling and wear monitoring in high speed milling
- (2018) Kunpeng Zhu et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
- (2017) Anuj Karpatne et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests
- (2017) Dazhong Wu et al. JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME
- The WEAR methodology for prognostics and health management implementation in manufacturing
- (2017) Stephen Adams et al. JOURNAL OF MANUFACTURING SYSTEMS
- Study of spindle power data with neural network for predicting real-time tool wear/breakage during inconel drilling
- (2017) Raphael Corne et al. JOURNAL OF MANUFACTURING SYSTEMS
- An advanced numerical approach on tool wear simulation for tool and process design in metal cutting
- (2017) M. Binder et al. SIMULATION MODELLING PRACTICE AND THEORY
- Adaptive resampling-based particle filtering for tool life prediction
- (2015) Peng Wang et al. JOURNAL OF MANUFACTURING SYSTEMS
- Enhanced particle filter for tool wear prediction
- (2015) Jinjiang Wang et al. JOURNAL OF MANUFACTURING SYSTEMS
- Automated wear characterization for broaching tools based on machine vision systems
- (2015) Jamie Loizou et al. JOURNAL OF MANUFACTURING SYSTEMS
- New observations on tool wear mechanism in dry machining Inconel718
- (2011) Zhaopeng Hao et al. INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE
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