On the performance of expert-augmented machine learning with limited experimental data collected from powder particle characteristics used in laser powder bed fusion

Laser powder bed fusion (LPBF) is a metal additive manufacturing (AM) process where a part is created layer-by-layer through the melting and solidification of powder layers. The main challenge in LPBF is quality assurance. An estimation of powder rheology/flowability, as a quality-impacting factor, is therefore necessary prior to printing. To predict powder flowability based on morphological features without cost- and time-inefficient experiments, this study explores using machine learning algorithms (ML). It focuses on a sample set of standard and off-size Ti₆Al₄V (Ti₆₄) powders, aiming to (1) assess ML regressors and equation fitting for flowability prediction, using the small original dataset and (2) overcome data limitations’ impact on regression through data augmentation techniques. Results showed conventional ML regressors like support vector regression (SVR) sufficed for dynamic flowability prediction. Still, more complex targets such as shear flowability and hall flowability required innovative approaches like genetic programming (equation fitting) and synthetic minority over-sampling (SMOTE), respectively, as proposed in this work.