In the context of Laser Powder Bed Fusion (PBF-LB), the ability to detect and correct process drifts and anomalies has long been the "Holy Grail" of additive manufacturing. In-situ inspection promises near-real-time identification of flaws and non-conformances during production. Indeed, Lab-scale and commercial systems, many of which incorporate machine learning, have been developed and demonstrated to identify some process and part flaws. This presentation elucidates the underpinnings of current, commercial in-situ monitoring technologies and presents a real-world comparison of solutions from five companies— Addiguru, Additive Assurance, Applied Optimization, JENTEK Sensors, and Phase3D—carried out on as part of an ASTRO-ASTM-InSPIRE sponsored challenge. Furthermore, we demonstrate that combining high-resolution melt pool imaging with near-infrared long-exposure imaging enables the detection of flaws on the order of tens of microns in thin-walled lattice structures. Applications include quality assurance for heat exchangers and other thin-walled structural components.
Learning Objectives:
By presentation's end, participants will be able to define sensor types, including single-point detectors (e.g., photodiodes, ultrasonic transducers, microphones), arrays (e.g., eddy current arrays), optical and infrared imaging, and fringe-projection.
Following this presentation, learners will be able to explain the state-of-the-art for commercial in-situ technologies and their applications for flaw detection in thin-walled structures.
