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WFRF:(Rahman Hamidur)
 

Sökning: WFRF:(Rahman Hamidur) > (2022) > Artificial Intellig...

LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00004310naa a2200409 4500
001oai:DiVA.org:liu-190750
003SwePub
008221227s2022 | |||||||||||000 ||eng|
024a https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1907502 URI
024a https://doi.org/10.1109/access.2022.32306372 DOI
040 a (SwePub)liu
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a for2 swepub-publicationtype
100a Rahman, Hamiduru School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden4 aut
2451 0a Artificial Intelligence-Based Life Cycle Engineering in Industrial Production :b A Systematic Literature Review
264 1b Institute of Electrical and Electronics Engineers (IEEE),c 2022
338 a electronic2 rdacarrier
500 a 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
520 a 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.
650 7a NATURVETENSKAPx Data- och informationsvetenskapx Datorteknik0 (SwePub)102062 hsv//swe
650 7a NATURAL SCIENCESx Computer and Information Sciencesx Computer Engineering0 (SwePub)102062 hsv//eng
653 a Articial intelligence
653 a life cycle engineering
653 a machine learning
653 a sustainable development goal
700a D'Cruze, Ricky Stanleyu School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden4 aut
700a Ahmed, Mobyen Uddinu School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden4 aut
700a Sohlberg, Rickardu School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden4 aut
700a Sakao, Tomohiko,d 1969-u Linköpings universitet,Industriell miljöteknik,Tekniska fakulteten4 aut0 (Swepub:liu)tomsa86
700a Funk, Peteru School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden4 aut
710a School of Innovation, Design and Engineering, Mälardalen University, Västerås, Swedenb Industriell miljöteknik4 org
773t IEEE Accessd : Institute of Electrical and Electronics Engineers (IEEE)g 10, s. 133001-133015q 10<133001-133015x 2169-3536
856u https://liu.diva-portal.org/smash/get/diva2:1722183/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-190750
8564 8u https://doi.org/10.1109/access.2022.3230637

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