Journal ArticleDOI
Anomaly Detection and Classification in a Laser Powder Bed Additive Manufacturing Process using a Trained Computer Vision Algorithm
Luke Scime,Jack Beuth +1 more
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TLDR
A computer vision algorithm is used to automatically detect and classify anomalies that occur during the powder spreading stage of the process, which has the potential to become a component of a real-time control system in an LPBF machine.Abstract:
Despite the rapid adoption of laser powder bed fusion (LPBF) Additive Manufacturing by industry, current processes remain largely open-loop, with limited real-time monitoring capabilities. While some machines offer powder bed visualization during builds, they lack automated analysis capability. This work presents an approach for in-situ monitoring and analysis of powder bed images with the potential to become a component of a real-time control system in an LPBF machine. Specifically, a computer vision algorithm is used to automatically detect and classify anomalies that occur during the powder spreading stage of the process. Anomaly detection and classification are implemented using an unsupervised machine learning algorithm, operating on a moderately-sized training database of image patches. The performance of the final algorithm is evaluated, and its usefulness as a standalone software package is demonstrated with several case studies.read more
Citations
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Journal ArticleDOI
In-Situ Monitoring for Defect Identification in Nickel Alloy Complex Geometries Fabricated by L-PBF Additive Manufacturing
J. Logan McNeil,Kevin Sisco,Curt Frederick,Michael Massey,Keith Carver,Fred List,Caian Qiu,Morgan Mader,Suresh Sundarraj,S. Suresh Babu +9 more
TL;DR: In this paper, the ability to detect spatial distributions of defects is explored using in-situ monitoring of thermal signatures and surfaces and the observed thermal signatures were also verified with an analytical model for layer-wise heat transfer simulation that is sensitive to laser raster scan strategies.
Journal ArticleDOI
Metal-based additive manufacturing condition monitoring methods: From measurement to control
TL;DR: In this article, the authors present a survey of the state-of-the-art metal-based additive manufacturing (MAM) process monitoring and control systems, and discuss the advantages and disadvantages of their algorithmic implementations and applications.
Journal ArticleDOI
Automated layerwise detection of geometrical distortions in laser powder bed fusion
TL;DR: In this article, a statistical process monitoring approach is proposed to detect the onset of geometrical distortions during the build by comparing the slice contour reconstruction with the nominal geometry in each layer.
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Proceedings ArticleDOI
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TL;DR: This paper presents an automated method to classify 3Dprinted polymer parts as either good or defective based on images captured during Fused Filament Fabrication (FFF), using independent machine learning and deep learning approaches.
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