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.

本文引入了一个全面的机器学习框架，利用物理感知型的 MAM 特征化、多功能机器学习模型和评估指标，预测熔池缺陷和几何形状，为熔池控制和工艺优化提供了基础。此外，还发现了基于数据驱动的显式模型，用于从工艺参数和材料性质估算熔池几何形状。