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
Reads0
Chats0
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
More filters
Posted Content
Tackling Climate Change with Machine Learning
David Rolnick,Priya L. Donti,Lynn H. Kaack,K. Kochanski,Alexandre Lacoste,Kris Sankaran,Andrew S. Ross,Nikola Milojevic-Dupont,Natasha Jaques,Anna Waldman-Brown,Alexandra Luccioni,Tegan Maharaj,Evan D. Sherwin,S. Karthik Mukkavilli,Konrad P. Kording,Carla P. Gomes,Andrew Y. Ng,Demis Hassabis,John Platt,Felix Creutzig,Jennifer Chayes,Yoshua Bengio +21 more
TL;DR: From smart grids to disaster management, high impact problems where existing gaps can be filled by ML are identified, in collaboration with other fields, to join the global effort against climate change.
Journal ArticleDOI
Machine learning in additive manufacturing: State-of-the-art and perspectives
TL;DR: A comprehensive review on the state-of-the-art of ML applications in a variety of additive manufacturing domains can be found in this paper, where the authors provide a section summarizing the main findings from the literature and provide perspectives on some selected interesting applications.
Journal ArticleDOI
Mechanistic models for additive manufacturing of metallic components
TL;DR: In this article, the authors focus on the available mechanistic models of additive manufacturing (AM) that have been adequately validated and evaluate the functionality of AM models in understanding of the printability of commonly used AM alloys and the fabrication of functionally graded alloys.
Journal ArticleDOI
A review on machine learning in 3D printing: applications, potential, and challenges
TL;DR: In the authors’ perspective, in situ monitoring of AM processes will significantly benefit from the object detection ability of ML, and data sharing of AM would enable faster adoption of ML in AM.
Journal ArticleDOI
Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process
Luke Scime,Jack Beuth +1 more
TL;DR: In this article, a visible-light high speed camera with a fixed field of view is used to study the morphology of L-PBF melt pools in the Inconel 718 material system.
References
More filters
Book
The scientist and engineer's guide to digital signal processing
TL;DR: Getting Started with DSPs 30: Complex Numbers 31: The Complex Fourier Transform 32: The Laplace Transform 33: The z-Transform Chapter 27 Data Compression / JPEG (Transform Compression)
Proceedings ArticleDOI
SUN database: Large-scale scene recognition from abbey to zoo
TL;DR: This paper proposes the extensive Scene UNderstanding (SUN) database that contains 899 categories and 130,519 images and uses 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition and establish new bounds of performance.
Journal ArticleDOI
Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing
Sarah K. Everton,Sarah K. Everton,Matthias Hirsch,Petros Stravroulakis,Richard Leach,Adam T. Clare +5 more
TL;DR: In this paper, the state-of-the-art with respect to inspection methodologies compatible with additively manufactured (AM) processes is explored with the intention of identifying new avenues for research and proposing approaches to integration into future generations of AM systems.
Journal ArticleDOI
A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing
Gustavo Tapia,Alaa Elwany +1 more
Journal ArticleDOI
Process defects and in situ monitoring methods in metal powder bed fusion: a review
TL;DR: In this paper, a review of the literature and the commercial tools for insitu monitoring of powder bed fusion (PBF) processes is presented, focusing on the development of automated defect detection rules and the study of process control strategies.