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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A predictive model for in situ distortion correction in laser powder bed fusion using laser shock peen forming
TL;DR: In this paper, a hybrid metal additive manufacturing (AM) method that integrates in situ laser shock peen (LSP) forming with laser powder bed fusion (PBF) to mitigate vertical distortions during part builds is proposed.
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Automated porosity assessment of parts produced by Laser Powder Bed Fusion using Convolutional Neural Networks
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A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management
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Effect of Environmental Factors on the Accuracy of a Quality Inspection System Based on Transfer Learning
TL;DR: In this paper, the authors proposed a method to find the best solution to the problem of the lack of resources in the field of information technology in order to improve the quality of information.
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Current Status and Challenges of Powder Bed Fusion-Based Metal Additive Manufacturing: Literature Review
TL;DR: In this article , the state of the art in powder bed fusion (PBF) and technological challenges, with a focus on selective laser melting (SLM), are discussed, focusing mainly on articles that emphasize the status and challenges of PBF metal-based AM, with special attention given to the process parameters and flaws as a determining factor for printed part quality and reliability.
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