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research-article

In-process Monitoring of Material Cross-Contamination Defects in Laser Powder Bed Fusion

[+] Author and Article Information
Mohammad Montazeri

Mechanical and Materials Engineering Department, University of Nebraska-Lincoln, Lincoln, Nebraska, 68516
mmontazeri@huskers.unl.edu

Reza Yavari

Mechanical and Materials Engineering Department, University of Nebraska-Lincoln, Lincoln, Nebraska, 68516
mrezayavari89@gmail.com

Prahalada Rao

Mechanical and Materials Engineering Department, University of Nebraska-Lincoln, Lincoln, Nebraska, 68516
rao@unl.edu

Paul Boulware

Edison Welding Institute (EWI), Columbus, Ohio, 43221
pboulware@ewi.org

1Corresponding author.

ASME doi:10.1115/1.4040543 History: Received November 13, 2017; Revised June 05, 2018

Abstract

The goal of this work is to detect the onset of material cross-contamination in laser powder bed fusion (L-PBF) additive manufacturing (AM) process using data from in-situ sensors. Material cross-contamination refers to trace foreign materials that may be introduced in the powder feedstock due to reasons, such as poor cleaning of the AM machine after previous builds, or inadequate quality control during production and storage of the feedstock powder material. Material cross-contamination may lead to deleterious changes in the microstructure of the AM part and consequently affect its functional properties. Accordingly, the objective of this work is to develop and apply a spectral graph theoretic approach to detect the occurrence of material cross-contamination in real-time during the build using in-process sensor signatures, such as those acquired from a photodetector. Inconel alloy 625 test parts were made on a custom-built L-PBF apparatus integrated with multiple sensors, including a photodetector (300 nm to 1100 nm). During the process the powder bed was contaminated with two types of foreign materials, namely, tungsten and aluminum powders under varying degrees of severity. Material cross-contamination is detected by tracking the process signatures from the photodetector sensor hatch-by-hatch invoking spectral graph transform coefficients. These coefficients are subsequently traced on a Hoteling statistical control chart. Using this approach, the error in detecting the onset of material cross-contamination was < 5% , in contrast, traditional stochastic time series modeling approaches had corresponding error exceeding 15%.

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