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research-article

From Process Condition to Build Quality through Modeling and Monitoring of In-process Layerwise Images in Laser Powder Bed Fusion Additive Manufacturing Process

[+] Author and Article Information
Farhad Imani

Industrial and Manufacturing Engineering, Pennsylvania State University, State College, PA
fxi1@psu.edu

Aniruddha Gaikwad

Mechanical and Materials Engineering, University of Nebraska-Lincoln, Lincoln, NE
acgaikwad@gmail.com

Mohammad Montazeri

Mechanical and Materials Engineering, University of Nebraska-Lincoln, Lincoln, NE
mmontazeri@huskers.unl.edu

Prahalada Rao

Mechanical and Materials Engineering, University of Nebraska-Lincoln, Lincoln, NE
rao@unl.edu

Hui Yang

Industrial and Manufacturing Engineering, Pennsylvania State University, State College, PA
huy25@psu.edu

Edward Reutzel

Applied Research Laboratory, Pennsylvania State University, State College, PA
ewr101@arl.psu.edu

1Corresponding author.

ASME doi:10.1115/1.4040615 History: Received September 14, 2017; Revised June 19, 2018

Abstract

The goal of this work is to understand the effect of process conditions on part porosity in laser powder bed fusion (LPBF) Additive Manufacturing (AM) process, and subsequently, detect the onset of process conditions that lead to porosity from in-process sensor data. In pursuit of this goal, the objectives of this work are two-fold: (1) Quantify the frequency (number), size and location of pores as a function of three LPBF process parameters, namely, the hatch spacing (H), laser velocity (V), and laser power (P). (2) Monitor and identify process conditions that are liable to cause porosity using in-process optical images of the powder bed invoking multifractal and spectral graph theoretic analysis. To achieve the first objective, titanium alloy (Ti-6Al-4V) test cylinders owere built under differing H, V, and P settings on a commercial LPBF machine (EOS M280). The effect of these parameters on frequency, size and location of pores was quantified based on X-ray computed tomography (XCT) images. To achieve the second objective, layerwise optical images of the powder bed were acquired as the parts were being built. Spectral graph theoretic and multifractal features were extracted from the layer-by-layer images for each test part. Subsequently, these features were linked to the process parameters using machine learning approaches. Through these image-based features process conditions under which the parts were built was identified with the statistical fidelity over 80% (F-score).

Copyright (c) 2018 by ASME
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