Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process

By Luke Scime, Jack L. Beuth. Additive Manufacturing, 2019

352 citations recorded by the source corpus.

Abstract Because many of the most important defects in Laser Powder Bed Fusion (L-PBF) occur at the size and timescales of the melt pool itself, the development of methodologies for monitoring the melt pool is critical. This works examines the possibility of in-situ detection of keyholing porosity and balling instabilities. Specifically, a visible-light high speed camera with a fixed field of view is used to study the morphology of L-PBF melt pools in the Inconel 718 material system. A scale-invariant description of melt pool morphology is constructed using Computer Vision techniques and unsupervised Machine Learning is used to differentiate between observed melt pools. By observing melt pools produced across process space, in-situ signatures are identified which may indicate flaws such as those observed ex-situ. This linkage of ex-situ and in-situ morphology enabled the use of supervised Machine Learning to classify melt pools observed (with the high speed camera) during fusion of non-bulk geometries such as overhangs.

Subfields: Automotive Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/281285931883f2ddc6480050e9c5581772721de9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. W. Y. Yeong
2. Swee Leong Sing
3. Guo Dong Goh
4. Xuxiao Li
5. N. Kouraytem
6. Chao Wei
7. Zhizhou Zhang
8. Lin Li

In Mechanical Engineering

1. W. Y. Yeong
2. Swee Leong Sing
3. Guo Dong Goh
4. Lin Li
5. Chao Wei
6. Xuxiao Li
7. N. Kouraytem
8. Zhizhou Zhang

Citations in the corpus

• A review on machine learning in 3D printing: applications, potential, and challenges (2020, methodology): “The feature vector is then fed to SVM image claissification algorithm to learn the defects such as under-melting, keyhol-ing, and balling (Scime and Beuth 2019).”
• In-situ sensing, process monitoring and machine control in Laser Powder Bed Fusion: A review (2021, neutral): “For example, the use of in-situ imaging and ML has allowed for the prediction of part properties such as geometric defects [146] and porosity and balling [147], or identification of process issues such as recoating errors [148] and overmelting [51,149].”
• Modeling process–structure–property relationships in metal additive manufacturing: a review on physics-driven versus data-driven approaches (2020, methodology): “For example, Scime and Beuth [77, 78] and Caggiano et al [79] employed classification techniques in identifying defects such as keyhole-induced pores and balling from in situ process images.”
• An overview of laser-based multiple metallic material additive manufacturing: from macro- to micro-scales (2020, methodology): “At the same time, use phase transition analysis software such as CALPHAD to predict the phase transition and eliminate the proportion of material components that will produce harmful phases; During the experiment, using in-situ monitoring approaches, for instance, infrared imaging camera, high-speed”