Predicting additive manufacturing defects with robust feature selection for imbalanced data

Ethan Houser, Sara Shashaani, Ola Harrysson and Yongseok Jeon

IISE Transactions, 2024, vol. 56, issue 9, 1001-1019

Abstract: Promptly predicting defects during an additive manufacturing process using only copious log data provides many advantages, albeit with computational limitations. We focus on predicting defects during electron beam melting with the black box nature of the manufacturing machine. For an accurate prediction of defects, which are rare (

Date: 2024
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DOI: 10.1080/24725854.2023.2207633

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