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
Powder-Bed Fusion Process Monitoring by Machine Vision With Hybrid Convolutional Neural Networks
TL;DR: A method of hybrid convolutional neural networks (CNNs) is proposed for powder-bed fusion (PBF) process monitoring and it is found that the temporal information for PBF process monitoring by the vision detection of the process zone is significant.
Journal ArticleDOI
Characterization of in-situ measurements based on layerwise imaging in laser powder bed fusion
TL;DR: In this article, the accuracy of in-situ contour identification in laser powder bed fusion (LPBF) layerwise images by means of a measurement system performance characterization is evaluated.
Journal ArticleDOI
Real-Time 3D Printing Remote Defect Detection (Stringing) with Computer Vision and Artificial Intelligence
TL;DR: A novel methodology for the quality assessment of a Fused Filament Fabrication (FFF) 3D printing object during the printing process through AI-based Computer Vision using deep neural networks for the recognition of stringing.
Proceedings ArticleDOI
Investigation of Deep Learning for Real-Time Melt Pool Classification in Additive Manufacturing
TL;DR: Results show the feasibility of the proposed method for a real-time closed loop control of L-PBF process, and a deep learning-based melt pool classification method is developed to analyze melt pool size both fast and accurately.
Journal ArticleDOI
Synthetic data augmentation for surface defect detection and classification using deep learning
TL;DR: A novel framework is proposed for data augmentation by creating synthetic images using Generative Adversarial Networks (GANs) to improve the performance of CNN for classification of surface defects and demonstrates high generalization capability.
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