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Artificial Intelligence-Based Life Cycle Engineering in Industrial Production: A Systematic Literature Review
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1547-4386
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0002-0773-0466
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
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2022 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 133001-133015Article, review/survey (Refereed) Published
Abstract [en]

For the last few years, cases of applying artificial intelligence (AI) to engineering activities towards sustainability have been reported. Life Cycle Engineering (LCE) provides a potential to systematically reach higher and productivity levels, owing to its holistic perspective and consideration of economic and environmental targets. To address the current gap to more systematic deployment of AI with LCE (AI-LCE) we have performed a systematic literature review emphasizing the three aspects:(1) the most prevalent AI techniques, (2) the current AI-improved LCE subfields and (3) the subfields with highly enhanced by AI. A specific set of inclusion and exclusion criteria were used to identify and select academic papers from several fields, i.e. production, logistics, marketing and supply chain and after the selection process described in the paper we ended up with 42 scientific papers. The study and analysis show that there are many AI-LCE papers addressing Sustainable Development Goals mainly addressing: Industry, Innovation, and Infrastructure; Sustainable Cities and Communities; and Responsible Consumption and Production. Overall, the papers give a picture of diverse AI techniques used in LCE. Production design and Maintenance and Repair are the top explored LCE subfields whereas logistics and Procurement are the least explored subareas. Research in AI-LCE is concentrated in a few dominating countries and especially countries with a strong research funding and focus on Industry 4.0; Germany is standing out with numbers of publications. The in-depth analysis of selected and relevant scientific papers are helpful in getting a more correct picture of the area which enables a more systematic approach to AI-LCE in the future.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2022. Vol. 10, p. 133001-133015
Keywords [en]
Artificial intelligence, life cycle engineering, machine learning, sustainable development, sustainable development goal
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-62977DOI: 10.1109/ACCESS.2022.3230637ISI: 000905683300001Scopus ID: 2-s2.0-85146250639OAI: oai:DiVA.org:mdh-62977DiVA, id: diva2:1764403
Available from: 2023-06-08 Created: 2023-06-08 Last updated: 2023-06-08Bibliographically approved

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Rahman, HamidurAhmed, Mobyen UddinSohlberg, RickardFunk, Peter

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Rahman, HamidurD'Cruze, Ricky StanleyAhmed, Mobyen UddinSohlberg, RickardSakao, TomohikoFunk, Peter
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