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
Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control
Sachin Kumar,T. Gopi,N. Harikeerthana,Manish Gupta,Vidit Gaur,Grzegorz Królczyk,Chuansong Wu +6 more
TL;DR: A state-of-the-art review of machine learning techniques for additive manufacturing can be found in this paper , where various categories especially focus on design, processes and production control of additive manufacturing are described.
Journal ArticleDOI
Deep learning-assisted real-time defect detection and closed-loop adjustment for additive manufacturing of continuous fiber-reinforced polymer composites
TL;DR: In this paper , a deep learning approach is successfully developed for the online detection of defects, and the defects are effectively controlled by closed-loop adjustment of process parameters, which is never achievable in any conventional methods of composite fabrication.
Journal ArticleDOI
Image Processing of Mg-Al-Sn Alloy Microstructures for Determining Phase Ratios and Grain Size and Correction with Manual Measurement
Ali Erçetin,Fatih Akkoyun,Ercan Şimşir,Danil Yurievich Pimenov,Khaled Giasin,Manjunath Patel Gowdru Chandrashekarappa,Avinash Lakshmikanthan,Szymon Wojciechowski +7 more
TL;DR: In this paper, the intermetallic phase ratio and grain size in the microstructure of known Mg-Sn-Al alloys were measured by computer vision (CV) technology.
Journal ArticleDOI
Motion Feature Based Melt Pool Monitoring For Selective Laser Melting Process
TL;DR: In this paper , a new motion feature is introduced to describe the moving melt pool, and the distance between the centroid and the boundary of melt pool is calculated from the unfolded clockwise at a step angle, which constructs a high dimensional feature vector as the motion features.
Journal ArticleDOI
Securing cyber-physical additive manufacturing systems by in-situ process authentication using streamline video analysis
TL;DR: Wang et al. as mentioned in this paper proposed a process authentication methodology based on image texture analysis of the layer-wise in-situ videos, which is characterized as the layerwise texture descriptor tensor (LTDT).
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