MeltPoolGAN: Auxiliary Classifier Generative Adversarial Network for melt pool classification and generation of laser power, scan speed and scan direction in Laser Powder Bed Fusion
Jan Petrik, Barış Kavas, Markus Bambach�
Abstract
A reliable classification architecture for melt pool images is crucial for real-time monitoring and control in additive manufacturing. It enables for the identification of discrepancies between actual and desired process parameters, which could lead to defects in printed part. Moreover, adjusting process parameters according to classification results promotes consistent melt pool geometry and leads to enhanced part quality. In addition to the classification, controlled generation of melt pool images can be employed for data generation, training of classifiers, and offline process parameter optimization along the scan path. This study introduces MeltPoolGAN, a novel machine-learning architecture designed for handling both of these tasks. The architecture is able to classify up to 371 classes consisting of process parameters such as laser power, scan speed and scan direction. This is a significant increase in the total number of classes as well as process parameters compared to state-of-the-art that only classified a maximum of 6 classes, consisting of a single process parameter. With this input, the MeltPoolGAN reaches accuracies of around 97% for power and scan speed class classification, and scan direction estimation errors below 3 degrees making it a reliable solution. Furthermore, the MeltPoolGAN is able to generate plausible melt pool images in controlled and flexible manner based on disentangled input parameters. Finally, since the architecture was trained and tested on two distinct datasets, the publicly available NIST dataset and the in-house created ETH dataset, its robustness and capabilities of being a general-purpose AI model are also demonstrated.