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Predicting additive manufacturing defects with robust feature selection for imbalanced data

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  • Ethan Houser
  • Sara Shashaani
  • Ola Harrysson
  • Yongseok Jeon

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 (

Suggested Citation

  • Ethan Houser & Sara Shashaani & Ola Harrysson & Yongseok Jeon, 2024. "Predicting additive manufacturing defects with robust feature selection for imbalanced data," IISE Transactions, Taylor & Francis Journals, vol. 56(9), pages 1001-1019, September.
    • Handle: RePEc:taf:uiiexx:v:56:y:2024:i:9:p:1001-1019
    DOI: 10.1080/24725854.2023.2207633
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