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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.
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Hu et al. (2024) studied this question.