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

ABSTRACT: 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 statistical processes or feedstock control, a sequence of machine learning models was trained on 6299 tensile specimens to locally predict the tensile properties of stainless-steel parts based on fused multi-modal in situ sensor data and a priori information. A cyberphysical infrastructure enabled the robust spatial tracking of individual specimens, and computer vision techniques registered the ground truth tensile measurements to the in situ data. The co-registered 230 GB dataset used in this work

SUBMITTER: Scime L 

PROVIDER: S-EPMC10707661 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature