A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning
- Submitting institution
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University of Greenwich
- Unit of assessment
- 12 - Engineering
- Output identifier
- 23360
- Type
- D - Journal article
- DOI
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10.1016/j.cirp.2019.03.010
- Title of journal
- CIRP Annals ‚Äê Manufacturing Technology
- Article number
- -
- First page
- 487
- Volume
- 68
- Issue
- 1
- ISSN
- 0007-8506
- Open access status
- Access exception
- Month of publication
- -
- 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
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4
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This collaborative project was funded by National Science Foundation of China (Ref: 51775278 & U1537209). For the first time, a meta-learning method was proposed and tested with small number of samples for accurate prediction of cutting tool wear under varying cutting conditions. This top journal paper was also presented at the 69th CIRP General Assembly (19-21 August 2019, Birmingham). All papers were invited from worl-class researchers, and the acceptance was very competitive.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
