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
Unsupervised Defect Segmentation in Selective Laser Melting
TL;DR: Li et al. as mentioned in this paper proposed an unsupervised segmentation method to detect defects on additive manufactured surfaces, which requires only a single scanned image, and the proposed method has three modules, including feature learning, self-attention and clustering modules, which are responsible for extracting features, capturing global features, and assigning cluster labels.
Book ChapterDOI
Impacts of metal additive manufacturing on smart city infrastructure
TL;DR: In this paper , a case study of the development and design process of a particular structured light monitoring system and the associated measurement model used to bolster its measurement capabilities is presented, along with a review of state-of-the-art metal additive manufacturing (MAM) monitoring research designed to provide structural health assessments and detect MAM-specific material and structural defects.
Journal ArticleDOI
Data fusion analysis in the powder-bed fusion AM process monitoring by Dempster-Shafer evidence theory
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Deviation Detection in Production Processes based on Video Data using Unsupervised Machine Learning Approaches
TL;DR: In this paper , the authors investigated the transferability of unsupervised machine learning methods to the production domain and found that the results show that the two chosen un-supervised autoencoder architectures can be partially used for generic deviation detection in the production domains.
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