Mechanics of Materials Commons^™

Heterogeneous Sensor Data Fusion For Multiscale, Shape Agnostic Flaw Detection In Laser Powder Bed Fusion Additive Manufacturing, Benjamin Bevans, Christopher Barrett, Thomas Spears, Aniruddha Gaikwad, Alex Riensche, Harold (Scott) Halliday, Prahalada Rao

Department of Mechanical and Materials Engineering: Faculty Publications

We developed and applied a novel approach for shape agnostic detection of multiscale flaws in laser powder bed fusion (LPBF) additive manufacturing using heterogenous in-situ sensor data. Flaws in LPBF range from porosity at the micro-scale (< 100 μm), layer related inconsistencies at the meso-scale (100 μm to 1 mm) and geometry-related flaws at the macroscale (> 1 mm). Existing data-driven models are primarily focused on detecting a specific type of LPBF flaw using signals from one type of sensor. Such approaches, which are trained on data from simple cuboid and cylindrical-shaped coupons, have met limited success when used for detecting multiscale flaws in complex LPBF parts. The objective of this work is to develop a heterogenous sensor data fusion …