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.

翻译：本研究采用混合模型学习方法识别激光增材制造过程中的缺陷。通过融入基于物理的原理，确保模型对具有物理意义的参数变化保持敏感。实证评估通过分析两种增材制造工艺（定向能量沉积与激光粉末床熔融）的实际数据完成。此外，我们还研究了所开发框架在包含不同合金类型与实验参数信息的公开数据集上的性能。结果表明，物理引导的混合模型在解析增材制造系统底层物理行为方面具有潜力。