Physics-Informed Mixture Models and Surrogate Models for Precision Additive Manufacturing
This work addresses defect detection in additive manufacturing, which is incremental as it applies existing mixture model methods with physics guidance to a specific industrial domain.
The study tackled defect identification in laser-based additive manufacturing by developing physics-informed mixture models, achieving results that demonstrate the models' potential to analyze underlying physical behavior across multiple AM processes and alloy types.
In this study, we leverage a mixture model learning approach to identify defects in laser-based Additive Manufacturing (AM) processes. By incorporating physics based principles, we also ensure that the model is sensitive to meaningful physical parameter variations. The empirical evaluation was conducted by analyzing real-world data from two AM processes: Directed Energy Deposition and Laser Powder Bed Fusion. In addition, we also studied the performance of the developed framework over public datasets with different alloy type and experimental parameter information. The results show the potential of physics-guided mixture models to examine the underlying physical behavior of an AM system.