Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning
- Submitting institution
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City, University of London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 478
- Type
- D - Journal article
- DOI
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10.1038/s41598-019-50144-w
- Title of journal
- Scientific Reports
- Article number
- 13964
- First page
- -
- Volume
- 9
- Issue
- 1
- ISSN
- 2045-2322
- Open access status
- Compliant
- Month of publication
- September
- 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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6
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Listed as ‘Top 100 in Materials Science’ for the year 2019 by Nature’s Scientific Reports, the research resulted in a lightning-fast Machine Learning (ML) tool to predict the properties of a composite material within seconds using the microstructure image as the sole input. With more than 95% accuracy, the study is a first of its kind in composites community. The research gained attention at two international conferences (USA and Europe) and presented as Invited Seminar at TU Delft, Netherlands. The work inspired author’s two more journal manuscripts and further defined his research direction for his EPSRC Young Investigator Award proposal.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -