Fostering research and innovation in materials manufacturing for Industry 5.0: The key role of domain intertwining between materials characterization, modelling and data science

被引:12
作者
Charitidis, Costas [1 ]
Sebastiani, Marco [2 ]
Goldbeck, Gerhard [3 ,4 ]
机构
[1] Natl Tech Univ Athens, Sch Chem Engn, 9 HeroonPolytech str, Athens 15780, Greece
[2] Univ Roma Tre, Engn Dept, Via Vasca Navale 79, I-00146 Rome, Italy
[3] St Johns Innovat Ctr, Goldbeck Consulting Ltd, Cambridge CB4 0WS, England
[4] EMMC ASBL, Ave Louise 54, B-1050 Brussels, Belgium
基金
欧盟地平线“2020”;
关键词
Industry; 5; 0; Ontology; Modelling; Materials characterization; Data interoperability;
D O I
10.1016/j.matdes.2022.111229
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Recent advances in materials modelling, characterization and materials informatics suggest that deep integration of such methods can be a crucial aspect of the Industry 5.0 revolution, where the fourth industrial revolution paradigms are combined with the concepts of transition to a sustainable, human-centric and resilient industry. We pose a specific deep integration challenge beyond the ordinary multi-disciplinary modelling/characterization research approach in this short communication with research and innovation as drivers for scientific excellence. Full integration can be achieved by developing com-mon ontologies across different domains, enabling meaningful computational and experimental data integration and interoperability. On this basis, fine-tuning of adaptive materials modelling/characteriza-tion protocols can be achieved and facilitate computational and experimental efforts. Such interoperable and meaningful data combined with advanced data science tools (including machine learning and artifi-cial intelligence) become a powerful asset for materials scientists to extract complex information from the large amount of data generated by last generation characterization techniques. To achieve this ambi-tious goal, significant collaborative actions are needed to develop common, usable, and sharable digital tools that allow for effective and efficient twinning of data and workflows across the different materials modelling and characterization domains.(c) 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
引用
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页数:3
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