A novel variable selection approach based on co-linearity index to discover optimal process settings by analysing mixed data
- Submitting institution
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Swansea University / Prifysgol Abertawe
- Unit of assessment
- 12 - Engineering
- Output identifier
- 21495
- Type
- D - Journal article
- DOI
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10.1016/j.cie.2014.03.017
- Title of journal
- Computers & Industrial Engineering
- Article number
- -
- First page
- 217
- Volume
- 72
- Issue
- -
- ISSN
- 0360-8352
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2014
- 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
-
-
- Research group(s)
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2 - FMRI - Future Manufacturing Research Institute
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The novel variable selection method for noisy and high dimensional datasets has been used in industrial settings (H. Md. Roshan, Maynard Steel Casting Company, roshan@maynardsteel.com) to support diagnosis and root cause analysis of complex manufacturing processes to achieve continual process improvement and satisfy the requirements of ISO9001:2015. It has led to subsequent industrial collaborations with Crown Technology (Yew Onn Pang, YewOnn.PANG@eur.crowncork.com) and Tata Steel (Scientific Fellow, name/email withheld) to develop new data driven methods for improving efficiency of manufacturing processes, contributing towards the award of EPSRC Fellowship EP/S001387/1, £477,185.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
