Automatic Fault Detection for Selective Laser Melting using Semi-Supervised Machine Learning
- Submitting institution
-
The University of Liverpool
- Unit of assessment
- 12 - Engineering
- Output identifier
- 12626
- Type
- D - Journal article
- DOI
-
10.1016/j.addma.2019.01.006
- Title of journal
- Additive Manufacturing
- Article number
- -
- First page
- 42
- Volume
- 27
- Issue
- -
- ISSN
- 2214-7810
- Open access status
- Compliant
- Month of publication
- February
- 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
-
5
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work led to an EPSRC Impact Acceleration Account Award (Machine-Learnt Fault Detection for Additive Manufacturing) and a PhD funded by Renishaw [Nick.Jones@Renishaw.com], the outputs of which are currently the subject of a patent application (currently being discussed with University IP department).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -