期刊
ADDITIVE MANUFACTURING
卷 19, 期 -, 页码 114-126出版社
ELSEVIER
DOI: 10.1016/j.addma.2017.11.009
关键词
Additive manufacturing; Computer vision; Machine learning; In-situ monitoring; Powder spreading anomalies
资金
- Bettis Naval Nuclear Laboratory
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. (C) 2017 Elsevier B.V. All rights reserved.
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