Non-Destructive Testing (NDT), Eddy current sensing (EDC)
At the heart of our work lies the transformational power of additive manufacturing (AM). Recognized for its ability to create intricate parts, offer exceptional design flexibility, and tailor parts to specific needs, AM simultaneously minimizes material wastage. Despite its potential, the challenge of maintaining the quality and structural stability of the fabricated parts remains. Our research focuses on integrating non-destructive testing (NDT) techniques with the AM process, creating a powerful solution to this challenge. We're specifically exploring the further development and refinement of Eddy Current Testing technology (ECT) in Powder Bed Fusion Laser Beam Melting (PBF-LB/M) machinery. Our goal is to achieve control over the PBF-LB/M process layer-by-layer to identify and rectify any defects.
Our research process initiates with a focus on defect characterization. By harnessing the power of machine learning and artificial intelligence, we aim to construct a precise and robust system for defect detection and classification. This system will lay a solid foundation for our subsequent research stages.
The defect data gathered in this stage is consolidated with other machine-related data. This results in a comprehensive Statistical Process Control (SPC) system, capable of offline monitoring and eventually, real-time layerwise quality assurance. Our SPC system guarantees that the produced parts align with the desired standards and specifications.
In the concluding stage of our research, we aspire to push the boundaries of layerwise quality control. We aim to explore and implement strategies for healing specific defect categories like local porosity or minor sub-surface cracks.
Our research endeavors to integrate Eddy Current Technology (ECT) with Powder Bed Fusion Laser Beam Melting (PBF-LB/M) machinery. This integration promises remarkable improvements in the quality, dependability, and efficiency of additive manufacturing, significantly boosting the robustness and sustainability of the AM industry. We believe our research findings have the potential to revolutionize how AM is perceived and utilized across various high-value sectors, thus encouraging its broader adoption in modern manufacturing practices.