MeltpoolNet: Melt pool characteristic prediction in Metal Additive Manufacturing using machine learning

Abstract

Characterizing melt pool shape and geometry is essential in Metal Additive Manufacturing (MAM) to control the printing process, and avoid defects. Predicting melt pool flaws based on process parameters and powder material is difficult due to the complex nature of MAM processes. Machine learning (ML) techniques can be useful in connecting process parameters to the type of flaws in the melt pool. In this work, we introduced a comprehensive framework for benchmarking ML for melt pool characterization. An extensive experimental dataset has been collected from more than 80 MAM articles containing MAM processing conditions, materials, melt pool dimensions, melt pool modes and flaw types. We introduced physics-aware MAM featurization, versatile ML models, and evaluation metrics to create a comprehensive learning framework for melt pool defect and geometry prediction. This benchmark can serve as a basis for melt pool control and process optimization. In addition, data-driven explicit models have been identified to estimate melt pool geometry from process parameters and material properties. These models have been shown to outperform Rosenthal estimation for melt pool geometry while maintaining interpretability.