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research-article

Heterogeneous Sensor-based Build Condition Monitoring in Laser Powder Bed Fusion Additive Manufacturing Process using a Spectral Graph Theoretic Approach

[+] Author and Article Information
Mohammad Montazeri

Mechanical and Materials Engineering Department Nebraska Hall Lincoln, NE 68588-0526
mmontazeri@huskers.unl.edu

Prahalada Rao

Mechanical and Materials Engineering Department 303 East Nebraska Hall Lincoln, NE 68588
rao@unl.edu

1Corresponding author.

ASME doi:10.1115/1.4040264 History: Received January 09, 2018; Revised May 03, 2018

Abstract

The goal of this work is to monitor the laser powder bed fusion (LPBF) process using an array of heterogeneous sensors so that a record may be made of those temporal and spatial build locations where there is a high probability of defect formation. In pursuit of this goal, a commercial LPBF machine at the National Institute of Standards and Technology (NIST) was integrated with three types of sensors, namely, a photodetector, high-speed visible camera, and short wave infrared (SWIR) thermal camera with the following objectives: (1) to develop and apply a spectral graph theoretic approach to monitor the LPBF build condition from the data acquired by the three sensors; and (2) to compare results from the three different sensors in terms of their statistical fidelity in distinguishing between different build conditions. As a first-step towards detection of defects and process irregularities in LPBF, this work focuses on capturing and differentiating the distinctive thermal signatures that manifest in parts with overhang features. Overhang features can significantly constrain the heat flux may and lead to issues such as poor surface finish, distortion and microstructure inhomogeneity. The proposed approach isolates the thermal patterns belonging to bulk and overhang features in terms of the following statistical fidelity (F-score) approaching 95% from the SWIR thermal camera. In comparison, conventional signal analysis techniques - e.g., neural networks, were evaluated with F-score in the range of 40% to 60%.

Copyright (c) 2018 by ASME
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