Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural networks
- Submitting institution
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The University of Birmingham
- Unit of assessment
- 12 - Engineering
- Output identifier
- 73835604
- Type
- D - Journal article
- DOI
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10.1016/j.jmapro.2019.07.020
- Title of journal
- Journal of Manufacturing Processes
- Article number
- -
- First page
- 603
- Volume
- 45
- Issue
- -
- ISSN
- 1526-6125
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2019
- URL
-
-
- Supplementary information
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-
- 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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3
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first paper to present an automated defect classification methodology for TIG alloy welding using an HDR camera and Artificial Intelligence (AI). The validity of the Artificial Neural Networks (ANNs) that we developed was successfully evaluated. The research led to the development and demonstration of a customised system that has been used extensively by TWI for commercial purposes, including supporting consultancy requests by leading members of the organisation such as Rolls-Royce.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -