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 of Sub-surface and Internal Defects in Additive Manufacturing: A Review
Youssef AbouelNour,Nikhil Gupta +1 more
TL;DR: In this paper , an analysis of the acquired in-situ data from both imaging and acoustic methods is discussed, as well as the means of data processing, and the verification of results obtained from such data is presented.
Journal ArticleDOI
Improved quality assessment of colour surfaces for additive manufacturing based on image entropy
TL;DR: The extended method, based on the combination of the local image entropy and its variance with additional colour adjustment, is proposed in the paper, leading to the proper classification of 78 samples used during the experimental verification of the proposed approach.
Journal ArticleDOI
Research on in situ monitoring of selective laser melting: a state of the art review
TL;DR: This paper can provide theoretical support for the SLM intelligent monitoring field by monitoring the quality of the forming process by molten pool signal, temperature signal, sound signal, and scanning track.
Posted Content
Deep learning prediction of stress fields in additively manufactured metals with intricate defect networks
TL;DR: In this article, a U-Net model was used to predict the elastic stress fields in images of defect-containing metal microstructures, and the model was applied to real AM micro-structures with severe lack of fusion defects.
Journal ArticleDOI
In-situ monitoring in L-PBF: Opportunities and challenges
TL;DR: The opportunities and challenges related to in-situ sensing and monitoring solutions for zero-defect and first-time-right AM processes are reviewed, with a special focus on metal Powder Bed Fusion (PBF) processes.
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