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
Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data
TL;DR: The first direct measurements of the satellite content in powder samples are demonstrated, demonstrating the flexibility of instance segmentation and providing another method for measuring the particle size distribution.
Journal ArticleDOI
Mitigating Scattering Effects in Light-Based Three-Dimensional Printing Using Machine Learning
Shangting You,Jiaao Guan,Jeffrey Alido,Henry H. Hwang,Ronald Yu,Leilani Kwe,Hao Su,Shaochen Chen +7 more
TL;DR: A deep neural network-based machine learning technique was used to mitigate the scattering effect, where the NN was employed to study the highly sophisticated relationship between the input digital masks and their corresponding output 3D printed structures.
Journal ArticleDOI
Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives
TL;DR: In this article , the authors reviewed recent advances in Mechanistic-AI, defined as a methodology that combines the raw mathematical power of AI methods with mechanism-driven principles and engineering insights, with a focus on approaches that can improve data requirements, generalizability, explainability and capability to handle challenging and heterogeneous manufacturing data.
Journal ArticleDOI
Development of a defect-detection platform using photodiode signals collected from the melt pool of laser powder-bed fusion
TL;DR: In this paper, a defect-detection platform using in-situ monitoring of light intensity emitted from the melt pool of laser powder bed fusion (LPBF) to detect pores initiated from the lack of fusion phenomenon was developed.
Journal ArticleDOI
Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data.
Ryan Cohn,Elizabeth A. Holm +1 more
TL;DR: This paper demonstrates how to construct, use, and evaluate a high-performance unsupervised machine learning system for classifying images in a popular microstructural dataset and provides insight toward applying unsuper supervised machine learning techniques to problems of interest in materials science.
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