Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing

• Published in: Additive manufacturing Vol. 46; p. 102089
• Main Authors: Gunasegaram, D.R., Murphy, A.B., Barnard, A., DebRoy, T., Matthews, M.J., Ladani, L., Gu, D.
• Format: Journal Article
• Language: English
• Published: United States Elsevier B.V 01.10.2021; Elsevier
• Subjects: Additive manufacturing; Artificial intelligence; Digital twins; Industry 4.0; Machine learning; MATERIALS SCIENCE; Multiphysics modeling; Multiscale modeling; Multiphysics modeling; Machine learning; Industry 4.0; Additive manufacturing; Digital twins; Artificial intelligence; Multiscale modeling
• ISSN: 2214-8604; 2214-7810
• DOI: 10.1016/j.addma.2021.102089

Artificial intelligence (AI) embedded within digital models of manufacturing processes can be used to improve process productivity and product quality significantly. The application of such advanced capabilities particularly to highly digitalized processes such as metal additive manufacturing (AM) is likely to make those processes commercially more attractive. AI capabilities will reside within Digital Twins (DTs) which are living virtual replicas of the physical processes. DTs will be empowered to operate autonomously in a diagnostic control capacity to supervise processes and can be interrogated by the practitioner to inform the optimal processing route for any given product. The utility of the information gained from the DTs would depend on the quality of the digital models and, more importantly, their faster-solving surrogates which dwell within DTs for consultation during rapid decision-making. In this article, we point out the exceptional value of DTs in AM and focus on the need to create high-fidelity multiscale-multiphysics models for AM processes to feed the AI capabilities. We identify technical hurdles for their development, including those arising from the multiscale and multiphysics characteristics of the models, the difficulties in linking models of the subprocesses across scales and physics, and the scarcity of experimental data. We discuss the need for creating surrogate models using machine learning approaches for real-time problem-solving. We further identify non-technical barriers, such as the need for standardization and difficulties in collaborating across different types of institutions. We offer potential solutions for all these challenges, after reflecting on and researching discussions held at an international symposium on the subject in 2019. We argue that a collaborative approach can not only help accelerate their development compared with disparate efforts, but also enhance the quality of the models by allowing modular development and linkages that account for interactions between the various sub-processes in AM. A high-level roadmap is suggested for starting such a collaboration. •We point out the role played by physics-based mechanistic models in the creation of digital twins of the AM process.•We identify technical hurdles for the development and linking of these models, and the scarcity of experimental data.•We discuss the need for creating surrogate models using machine learning approaches forreal-time problem-solving.•We further identify non-technical barriers and difficulties in collaborating across different types of institutions.•We offer potential solutions for all these challenges, based on discussions held at aninternational symposium convened for the purpose.