Cyber Physical System and Big Data enabled energy efficient machining optimisation
- Submitting institution
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Coventry University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 19047105
- Type
- D - Journal article
- DOI
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10.1016/j.jclepro.2018.03.149
- Title of journal
- Journal of Cleaner Production
- Article number
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- First page
- 46
- Volume
- 187
- Issue
- -
- ISSN
- 0959-6526
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2018
- URL
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- Supplementary information
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- Request cross-referral to
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- 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)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Sponsored by the EU Smarter project (FP7-PEOPLE- 610675), a new deep learning-based optimisation algorithm was developed to minimise electricity consumption during high-precision machining processes for Small and Medium-sized Enterprises (SMEs). To the best of our knowledge, this was the first industrial experiment of applying Big Data analytics to improve machining sustainability in Europe. The developed system was deployed into two SMEs in the UK and Spain for industrial trial. 40% energy saving on average was achieved (contact: Iain Mcgregor, Iain.Mcgregor@inenco.com). Presented as a keynote in the 6th International Conference on Mechanical Engineering & Mechanics, 2017, France.
- Author contribution statement
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- Non-English
- No
- English abstract
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