Advancing Thermal Physics-Informed PointNet Distortion Prediction Capabilities in Wire Arc-Directed Energy Deposition

Abstract Additive manufacturing (AM) is a customizable layer-wise production process challenged by inherent process complexity, often leading to structural defects such as geometric deformation and distortion. A common cause of structural defects is uneven material expansion and contraction during the rapid heating and cooling phases. Various data types can monitor the process for defects, but many AM processes lack a unified in-process data collection and synchronization. Moreover, there is a gap in developing methods to utilize multimodal data to learn from previous experiments to save on the cost of resources. This work develops a thermal physics-informed PointNet methodology that addresses the lack of in situ data-driven distortion characterization models constrained by physical function by leveraging multimodal position, weld parameters, and thermal data. First, this research utilizes a digital environment developed in robot operating system 2 to merge and synchronize multimodal sensor data—including infrared thermal images, system joint states, and weld parameters—to collect in situ thermal point cloud data. Second, using a PointNet deep learning model accounts for various data types and sensor sources in point cloud data format and maps the relationship to distortion characterization. Finally, a physics-informed model training loss function is created with thermal expansion and contraction constraints using thermal expansion coefficients. The integration of these processes marks a significant step toward harnessing the power of physics-informed machine learning to better understand and predict distortions in AM, paving the way for more reliable and accurate manufacturing outcomes.