用物理引导的混合模型检测激光增材制造缺陷
Physics-Informed Mixture Models and Surrogate Models for Precision Additive Manufacturing
- 融合物理规律的混合模型识别制造缺陷
- 在两种实际工艺中验证，对参数变化敏感
- 适合增材制造质量控制与工艺优化者
本研究采用混合模型学习方法，识别基于激光的增材制造（AM）过程中的缺陷。通过融入物理原理，确保模型对有意义的物理参数变化具有敏感性。实证评估基于两种AM工艺的真实数据：定向能量沉积（Directed Energy Deposition）和激光粉末床熔融（Laser Powder Bed Fusion）。此外，还测试了该框架在包含不同合金类型和实验参数信息的公开数据集上的表现。结果表明，物理引导的混合模型能够有效揭示AM系统的内在物理行为。
原文摘要 · Abstract (English)
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.
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