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September 10, 2025Applied SciencesOpen Access

Physics-Based Data Augmentation Enables Accurate Machine Learning Prediction of Melt Pool Geometry

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The hybrid approach achieves high prediction accuracy, with the best model showing R2 values greater than 0.98.
Incorporating synthetic melt pool data significantly enhances model generalization, especially in unstable regions.
Three machine learning methods are utilized, including Multilayer Perceptron, Random Forest, and XGBoost.
This study highlights the potential for scalable solutions in intelligent process optimization and monitoring.

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Hybrid framework integrates a thermal model and machine learning to enhance melt pool prediction accuracy.

Liu et al. (2025) studied this question.

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