A Data-Driven Framework for Direct Local Tensile Property Prediction of Laser Powder Bed Fusion Parts

A Data-Driven Framework for Direct Local Tensile Property Prediction of Laser Powder Bed Fusion Parts

This article proposes a generalizable, data-driven framework for qualifying laser powder bed fusion additively manufactured parts using part-specific in situ data, including powder bed imaging, machine health sensors, and laser scan paths. To achieve part qualification without relying solely on stat...

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Bibliographic Details
Main Authors: Luke Scime, Chase Joslin, David A. Collins, Michael Sprayberry, Alka Singh, William Halsey, Ryan Duncan, Zackary Snow, Ryan Dehoff, Vincent Paquit
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Materials
Subjects:
laser powder bed fusion
tensile properties
machine learning
in situ monitoring
Online Access:https://www.mdpi.com/1996-1944/16/23/7293

Internet

https://www.mdpi.com/1996-1944/16/23/7293

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