MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning

TL;DR

This paper presents MeltpoolNet, a machine learning framework for predicting melt pool characteristics in metal additive manufacturing, using extensive data and physics-aware features to improve defect prediction and process control.

Contribution

Introduces a comprehensive ML benchmarking framework for melt pool characterization, including physics-aware features and explicit models that outperform traditional estimations.

Findings

ML models effectively predict melt pool flaws and geometry.

Physics-aware featurization enhances prediction accuracy.

Data-driven models outperform Rosenthal estimation.

Abstract

Characterizing meltpool shape and geometry is essential in metal Additive Manufacturing (MAM) to control the printing process and avoid defects. Predicting meltpool flaws based on process parameters and powder material is difficult due to the complex nature of MAM process. Machine learning (ML) techniques can be useful in connecting process parameters to the type of flaws in the meltpool. 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, meltpool dimensions, meltpool modes and flaw types. We introduced physics-aware MAM featurization, versatile ML models, and evaluation metrics to create a comprehensive learning framework for meltpool 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 meltpool geometry from process parameters and material properties which outperform Rosenthal estimation for meltpool geometry while maintaining interpretability.