This paper explores how Artificial Intelligence (AI) techniques integration into circular economy (CE) in product design phase. Designing products for circularity is a phenomenon, which is rising in manufacturing industries to develop and improve product sustainability as well as to drive extra cost prevention mechanisms in production. Circular product design strategies and similarly some guides have been developed by academia to help designers in industries to design products which match best with characteristics of circular products. In addition, implementation of AI technologies in CE solutions increases productivity through higher optimization and real-time data analysis. This research used qualitative research methodology as two stage process. First a literature review on concepts of circular material design and integration of AI in CE along with smart production. Secondly, framework on the key roles of AI in circular product design and the key enablers are proposed based on the findings of qualitative research and theoretical part of the study. The outcomes of the study suggest a positive influence on implementing AI techniques in CE solutions in manufacturing industries.
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4 May 2020
13TH INTERNATIONAL ENGINEERING RESEARCH CONFERENCE (13TH EURECA 2019)
27 November 2019
Selangor Darul Ehsan, Malaysia
Research Article|
May 04 2020
Key enablers for deploying artificial intelligence for circular economy embracing sustainable product design: Three case studies
Malahat Ghoreishi;
Malahat Ghoreishi
a)
1
LUT University, School of Business and Management
, 53850 Lappeenranta, Finland
a)Corresponding author: Malahat.ghoreishi@lut.fi
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Ari Happonen
Ari Happonen
b)
2
LUT University, School of Engineering Science
, 53850 Lappeenranta, Finland
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a)Corresponding author: Malahat.ghoreishi@lut.fi
AIP Conf. Proc. 2233, 050008 (2020)
Citation
Malahat Ghoreishi, Ari Happonen; Key enablers for deploying artificial intelligence for circular economy embracing sustainable product design: Three case studies. AIP Conf. Proc. 4 May 2020; 2233 (1): 050008. https://doi.org/10.1063/5.0001339
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