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
A digital twin hierarchy for metal additive manufacturing
TL;DR: In this paper , the authors present a digital twin hierarchy for metal additive manufacturing (AM) based on surrogate modeling, in-situ sensing, hardware control systems and intelligent control policies.
Journal ArticleDOI
Additively manufactured materials and structures: A state-of-the-art review on their mechanical characteristics and energy absorption
TL;DR: In this article , the authors provide a comprehensive review on the recent advances in additively manufactured materials and structures as well as their mechanical properties with an emphasis on energy absorption applications and highlight significant challenges and future directions in this area.
Posted Content
A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes
Arindam Paul,Mojtaba Mozaffar,Zijiang Yang,Wei-keng Liao,Alok Choudhary,Jian Cao,Ankit Agrawal +6 more
TL;DR: In this article, a data-driven model-based real-time control system for additive manufacturing (AM) is presented. But, the authors focus on the modeling of additive manufacturing processes.
Book ChapterDOI
Computer Vision Methods for Non-destructive Quality Assessment in Additive Manufacturing
TL;DR: On-line quality assessment of the 3D printed surfaces using image analysis methods seems to be a good alternative, allowing to detect the quality decrease and stop the printing process or correct the surface in case of minor distortions to save time, energy and material.
Proceedings ArticleDOI
Acoustic Anomaly Detection in Additive Manufacturing with Long Short-Term Memory Neural Networks
TL;DR: A machine learning system detecting flaws and errors of a printer with varying difficulty is proposed, and a Long Short- Term Memory model was trained and validated with multiple classes of relevant sounds during 3d printing.
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