WFRF:(Rahman Hamidur)
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- Rahman, HamidurSchool of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden (author)
Artificial Intelligence-Based Life Cycle Engineering in Industrial Production : A Systematic Literature Review
- Article/chapterEnglish2022
Publisher, publication year, extent ...
- Institute of Electrical and Electronics Engineers (IEEE),2022
- electronicrdacarrier
Numbers
- LIBRIS-ID:oai:DiVA.org:liu-190750
- https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-190750URI
- https://doi.org/10.1109/access.2022.3230637DOI
Supplementary language notes
- Language:English
- Summary in:English
Part of subdatabase
- SwePubSwePub
Classification
Notes
- Funding: Adapt 2030 (Adaptive lifecycle design by applying digitalization and AI techniques to production) (Grant Number: 2019-05589)10.13039/501100001858-VINNOVA, Sweden’s Innovation Agency
- For the last few years, cases of applying articial intelligence (AI) to engineering activitiestowards sustainability have been reported. Life Cycle Engineering (LCE) provides a potential to systematicallyreach higher and productivity levels, owing to its holistic perspective and consideration of economic andenvironmental 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 AItechniques, (2) the current AI-improved LCE subelds and (3) the subelds with highly enhanced by AI.A specic set of inclusion and exclusion criteria were used to identify and select academic papers fromseveral elds, i.e. production, logistics, marketing and supply chain and after the selection process describedin the paper we ended up with 42 scientic papers. The study and analysis show that there are manyAI-LCE papers addressing Sustainable Development Goals mainly addressing: Industry, Innovation, andInfrastructure; 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 andRepair are the top explored LCE subelds 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 strongresearch funding and focus on Industry 4.0; Germany is standing out with numbers of publications. Thein-depth analysis of selected and relevant scientic papers are helpful in getting a more correct picture ofthe area which enables a more systematic approach to AI-LCE in the future.
Subject headings and genre
- NATURVETENSKAP Data- och informationsvetenskap Datorteknik hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Computer Engineering hsv//eng
- Articial intelligence
- life cycle engineering
- machine learning
- sustainable development goal
Added entries (persons, corporate bodies, meetings, titles ...)
- D'Cruze, Ricky StanleySchool of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden (author)
- Ahmed, Mobyen UddinSchool of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden (author)
- Sohlberg, RickardSchool of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden (author)
- Sakao, Tomohiko,1969-Linköpings universitet,Industriell miljöteknik,Tekniska fakulteten(Swepub:liu)tomsa86 (author)
- Funk, PeterSchool of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden (author)
- School of Innovation, Design and Engineering, Mälardalen University, Västerås, SwedenIndustriell miljöteknik (creator_code:org_t)
Related titles
- In:IEEE Access: Institute of Electrical and Electronics Engineers (IEEE)10, s. 133001-1330152169-3536
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