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Metal additive manufacturing (AM) faces challenges in rapid selection and optimization of manufacturing parameters for desired part quality. As a more efficient alternative to experiments and high-fidelity physics-based models, data-driven modeling is effective in understanding process–structure–property relationships. This brief review explores data-driven modeling in metal AM, focusing on “process”, “structure”, and “property”, further identifying limitations in current applications and accordingly presenting future outlook on the possible advancements in this domain.
Hu et al. (Thu,) studied this question.