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
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References
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Journal ArticleDOI
Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks
TL;DR: In this article, a system was developed to characterize powder feedstock materials for metal additive manufacturing (AM) by applying computer vision and machine learning methods, where feature detection and description algorithms were applied to create a microstructural scale image representation that can be used to cluster, compare and analyze powder micrographs.
Journal ArticleDOI
Flaw detection in powder bed fusion using optical imaging
TL;DR: In this article, a method for detecting lack-of-fusion flaws in powder bed fusion (PBF) additive manufacturing of metal components is presented, where a binary template is created from the sliced 3D model of the part and optical image data is indexed to the part geometry.
Proceedings ArticleDOI
High resolution imaging for inspection of Laser Beam Melting systems
TL;DR: This work presents a high resolution imaging system for inspection of LBM systems which can be easily integrated into existing machines and shows that the system can detect topological flaws and is able to inspect the surface quality of built layers.
Integration of a thermal imaging feedback control system in electron beam melting
Emmanuel Rodriguez,Francisco Medina,David Espalin,Cesar A. Terrazas,Dan Muse,Chad Henry,Eric MacDonald,Ryan B. Wicker +7 more
TL;DR: A thermal imaging system using an infrared (IR) camera was incorporated in the fabrication process of an Arcam A2 Electron Beam Melting system to provide layer-by-layer feedback and ensure quality and defect free products.