Multiphysics multi-scale computational framework for linking process–structure–property relationships in metal additive manufacturing: a critical review

ABSTRACT

This review article provides a critical assessment of the progress made in computational modelling of metal-based additive manufacturing (AM) with emphasis on its ability to predict physical phenomena, concepts of microstructural evolution, residual stresses, role of multiple thermal cycles, and formation of multi-dimensional defects along with the achieved degree of experimental validation. The uniqueness of this article stems from the inclusion of comprehensive information on computational progress in the field of fusion-based, sintering-based, and mechanical deformation-based AM. A computational model's role in determining the process framework for the desired outcome of the set properties of the AM components is recognised while presenting the process-microstructure maps, thereby appraising computational ability towards the qualification of products. The inclusion of a detailed discussion on the bi-directional coupling of machine learning and physics-based computational models provides a futuristic roadmap for the digital twin of metal-based AM.

KEYWORDS:

• Computational modelling
• thermal model
• thermo-fluidic model
• thermo-mechanical model
• process–structure–property model
• physics informed machine learning
• metal additive manufacturing
• digital twin

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Authors acknowledge the infrastructure and support of Center for Agile and Adaptive and Additive Manufacturing (CAAAM) funded through State of Texas Appropriation: 190405-105-805008-220.