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
Registration and fusion of large-scale melt pool temperature and morphology monitoring data demonstrated for surface topography prediction in LPBF
TL;DR: In this paper , a machine learning aided image analysis method is employed to retrieve the spatial distribution of MPs within the corresponding part's coordinates system and then the MP signature maps (MPSMs) are reconstructed by mapping the STWIP measured MP signatures to the registered MP coordinates.
Journal ArticleDOI
In‐situ Curing of 3D printed Freestanding Thermosets
TL;DR: In this paper , a review of in-situ curing methods that can be integrated into direct-ink writing was discussed, including frontal polymerization, electromagnetic heating, photochemistry, electron beam, and resistance heating curing.
Journal ArticleDOI
Deep learning characterization of surface defects in the selective laser melting process
TL;DR: Li et al. as mentioned in this paper proposed a deep learning characterization method based on a detail-aware dilated convolutional neural network (DDCNN) incorporated with a fine details feature map extractor designed to obtain final fine semantic features.
Book ChapterDOI
In-Line Process Monitoring of Powder-Bed Fusion and Directed-Energy Deposition Processes
TL;DR: In this article, in-line process monitoring of the metal additive manufacturing (AM) methods of laser and electron beam (e-beam) powder-bed fusion (PBF) and directed energy deposition (DED) is discussed.
Journal ArticleDOI
Anomaly detection in laser powder bed fusion using machine learning: A review
Tayyaba Sahar,Muhammad Rauf,Ahmar Murtaza,Lehar Asip Khan,Hasan Ayub,Syed Muslim Jameel,Inam Ul Ahad +6 more
TL;DR: In this article , the authors provide an overview of L-PBF and outline the ML methods used for anomaly detection in LPBF, and explain how ML methods are being used as a step forward toward enabling the real-time process control of MAM and the process can be optimized for higher accuracy, lower production time, and less material waste.
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