A review of multi-scale modeling strategies for metal additive manufacturing with emphasis on computational cost and time efficiency

Additive Manufacturing (AM), also known as 3D printing, enables the fabrication of complex geometries through a layer-by-layer deposition process, offering significant advantages for industrial applications. However, the process introduces complex thermal cycles that can lead to defects. Numerical simulations play a crucial role in mitigating these issues, reducing the need for costly experimental trials during process optimization. This tool can be applied across multiple scales—from predicting microstructure evolution at the microscale and defect formation (porosity and lack of fusion) at the mesoscale to assess macroscale features like distortion and residual stress. However, accurately modeling the multi-physics phenomena involved in AM processes incurs high computational costs, particularly for large-scale components, limiting their practical application. To address these limitations, various strategies have been developed, including the inherent strain method, multi-scale modeling, and dynamic mesh adaptation. This review examines existing numerical simulation models for metal AM reported in the literature, with a focus on their applications. The models were categorized by scale, highlighting the capabilities and limitations of each, with particular emphasis on computational cost and time efficiency of macroscale simulation. Additionally, the paper provides theoretical insights and validated case studies, offering deeper insights into these techniques, their implementation strategies, and recent advancements in AM simulation methodologies.