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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Accurate three-dimensional printing
TL;DR: In this paper, the authors present three-dimensional (3D) printing methods, apparatuses, and systems using, inter alia, a controller that regulates formation of at least one 3D object (e.g., in real time during the 3D printing); and a non-transitory computer-readable medium facilitating the same.
Journal ArticleDOI
In situ measurements of layer roughness during laser powder bed fusion additive manufacturing using low coherence scanning interferometry
Philip J. Depond,Gabe Guss,Sonny Ly,Nicholas P. Calta,Dave Deane,Saad A. Khairallah,Manyalibo J. Matthews +6 more
TL;DR: In this article, layer-to-layer height measurements of additively manufactured 316L stainless steel using high speed spectral-domain optical coherence tomography (SD-OCT) are presented.
Machine Learning in Additive Manufacturing: A Review
Lingbin Meng,Brandon McWilliams,William Jarosinski,Hye-Yeong Park,Yeon-Gil Jung,Je-Hyun Lee,Jing Zhang +6 more
TL;DR: The latest applications of machine learning (ML) in the additive manufacturing (AM) field are reviewed and different types of ML tasks, including regression, classification, and clustering are classified.
Journal ArticleDOI
Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning
TL;DR: The results show that semi-supervised learning is a promising approach for the automatic certification of AM builds that can be implemented at a fraction of the cost currently required.
Journal ArticleDOI
Evolution of 316L stainless steel feedstock due to laser powder bed fusion process
Michael Heiden,Lisa Anne Deibler,Jeff M. Rodelas,Josh R. Koepke,Dan J. Tung,David J. Saiz,Bradley Howell Jared +6 more
TL;DR: In this paper, the authors present a systematic study of 316L powder properties from the virgin state through thirty powder reuses in the laser powder bed fusion process, and the resulting AM build mechanical properties were investigated for both powder states.
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