Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm

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...

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Bibliographic Details
Published in:Additive manufacturing Vol. 19; pp. 114 - 126
Main Authors: Scime, Luke, Beuth, Jack
Format: Journal Article
Language:English
Published: Elsevier B.V 01.01.2018
Subjects:
Additive manufacturing
Computer vision
In-situ monitoring
Machine learning
Powder spreading anomalies
Computer vision
Additive manufacturing
In-situ monitoring
Powder spreading anomalies
Machine learning
ISSN:2214-8604, 2214-7810
Online Access:Get full text
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Summary: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.
ISSN:2214-8604
2214-7810
DOI:10.1016/j.addma.2017.11.009