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This is a Quantitative Study study.

October 23, 2025Open Access

Correcting Semi-Analytical Models with Residual Machine Learning Methods for Melt-Pool Prediction in PBF-LB/M

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Mean absolute percentage error improves significantly, reaching 4–6% in width and 2–5% in depth.
Evaluation employed machine-learning with random forests and achieved notable accuracy gains.
Semi-analytical models were corrected using machine-learning methods for efficient processing.
Findings underscore the importance of enhanced accuracy in part quality metrics like density and microstructure.

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Observational analysis improves melt-pool prediction accuracy in PBF-LB/M, suggesting machine-learning enhances process stability.

Rivas et al. (2025) studied this question.

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