| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
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| Symposium
|
Materials Genome, CALPHAD, and a Career over the Span of 20, 50, and 60 Years: An FMD/SMD Symposium in Honor of Zi-Kui Liu
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| Presentation Title |
Big Data-Assisted Digital Twins for the Smart Design and Manufacturing of Advanced Materials: From Atoms to Products |
| Author(s) |
William Yi Wang, Jinshan Li, Xingyu Gao, Feng Sun, Qinggong Jia, Bin Tang, Xi-Dong Hui, Haifeng Song, Zi-Kui Liu |
| On-Site Speaker (Planned) |
William Yi Wang |
| Abstract Scope |
Motivated by the ever-increasing wealth of data boosted by national strategies in terms of data-driven ICME, Materials Genome Engineering, Materials Genome Infrastructures, Industry 4.0, Materials 4.0 and so on, materials informatics represents a unique strategy in revealing the fundamental relationships in the development and manufacturing of advanced materials. Materials developments are becoming ever more integrated with robust data-driven and data-intensive technologies. In the present review, big data-assisted digital twins (DTs) for the smart design and manufacturing of advanced materials are presented from the perspective of the digital thread. Our recent works on the design, manufacturing and product service via big data-assisted DTs for smart design and manufacturing by integrating some of these advanced concepts and technologies. It is believed that big data-assisted DTs for smart design and manufacturing effectively support better products with the application of novel materials by reducing the time and cost of materials design and deployment. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
ICME, Machine Learning, Computational Materials Science & Engineering |