Mechanical & Materials Engineering, Department of
Date of this Version
3-23-2023
Citation
VIRTUAL AND PHYSICAL PROTOTYPING 2023, VOL. 18, NO. 1, e2196266 (27 pages) https://doi.org/10.1080/17452759.2023.2196266
Abstract
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 approach capable of detecting multiscale flaws across different LPBF part geometries and build conditions. Accordingly, data from an infrared camera, spatter imaging camera, and optical powder bed imaging camera were acquired across separate builds with differing part geometries and orientations (Inconel 718). Spectral graph-based process signatures were extracted from this heterogeneous thermo-optical sensor data and used as inputs to simple machine learning models. The approach detected porosity, layer-level distortion, and geometry-related flaws with statistical fidelity exceeding 93% (F-score).
Included in
Mechanics of Materials Commons, Nanoscience and Nanotechnology Commons, Other Engineering Science and Materials Commons, Other Mechanical Engineering Commons
Comments
Open access.