Enabling multimodal sensor fusion in additive manufacturing using a voxel-based data model

Abstract Modern manufacturing processes are monitored by different types of sensors throughout the entire process chain eventually sensing complementary pieces of information which results in the necessity to process multimodal sensor data. Specifically in the context of additive manufacturing, a combination of in-situ sensors and ex-situ measurement systems are used to capture both external contours and internal structures of the manufactured component. As a result of using heterogeneous measurement principles, the acquired sensor data is diverse and multimodal. Typically, this multimodal sensor data is analyzed independently from each other focusing on different quality characteristics. However, it is essential to consolidate this multimodal sensor data into a unified data model to achieve holistic quality assurance. This allows for evaluation of component quality at any stage in the manufacturing process, thereby enabling a more holistic approach to quality assurance. The aim of this study is to integrate multimodal sensor data into a newly developed consistent voxel-based data model. A holistic quality assurance for Fused Deposition Modeling can be realized in-situ by using a 2D camera and a laser light section sensor and ex-situ using X-ray computed tomography. The distinct datasets are then aligned and merged into a unified data model that incorporates both nominal and sensor-derived information. This unified voxel-based data model can serve in further investigations as the foundation for voxel-specific evaluation of the quality and the application of AI-driven quality analysis techniques.