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/).

Charitidis, C., Sebastiani, M., Goldbeck, G. (2022). Fostering research and innovation in materials manufacturing for Industry 5.0: The key role of domain intertwining between materials characterization, modelling and data science. MATERIALS & DESIGN, 223, 111229 [10.1016/j.matdes.2022.111229].

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

Sebastiani, M;
2022-01-01

Abstract

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/).
Charitidis, C., Sebastiani, M., Goldbeck, G. (2022). Fostering research and innovation in materials manufacturing for Industry 5.0: The key role of domain intertwining between materials characterization, modelling and data science. MATERIALS & DESIGN, 223, 111229 [10.1016/j.matdes.2022.111229].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/423408
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