A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning
- Submitting institution
-
Queen's University of Belfast
- Unit of assessment
- 12 - Engineering
- Output identifier
- 221053892
- Type
- D - Journal article
- DOI
-
10.1016/j.cirp.2019.03.010
- Title of journal
- CIRP Annals
- Article number
- -
- First page
- 487
- Volume
- 68
- Issue
- 1
- ISSN
- 0007-8506
- Open access status
- Deposit exception
- Month of publication
- April
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
A - Aeronautical, Mechanical, and Manufacturing
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The output led to the project “Digital Manufacturing and Intelligent Manufacturing” that was awarded by the National Science Fund of China for Distinguished Young Scholars in 2019. The output was presented as an invited keynote at CNCM 2019 (the 8th International Symposium of Computational Numerical Control Machining, 9-11th August 2019, Dalian, China). A tool wear monitoring system was developed based on the research outlined in this output, and has been applied in some Chinese manufacturing companies resulting in improved accuracy of tool wear prediction when compared to existing methods. (contact Yingguang Li, liyingguang@nuaa.edu.cn).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -