A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures

Yuwei Mao, Hui Lin, Christina Xuan Yu, Roger Frye, Darren Beckett, Kevin Anderson, Lars Jacquemetton, Fred Carter, Zhangyuan Gao, Wei keng Liao, Alok N. Choudhary, Kornel Ehmann, Ankit Agrawal*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Part quality manufactured by the laser powder bed fusion process is significantly affected by porosity. Existing works of process–property relationships for porosity prediction require many experiments or computationally expensive simulations without considering environmental variations. While efforts that adopt real-time monitoring sensors can only detect porosity after its occurrence rather than predicting it ahead of time. In this study, a novel porosity detection-prediction framework is proposed based on deep learning that predicts porosity in the next layer based on thermal signatures of the previous layers. The proposed framework is validated in terms of its ability to accurately predict lack of fusion porosity using computerized tomography (CT) scans, which achieves a F1-score of 0.75. The framework presented in this work can be effectively applied to quality control in additive manufacturing. As a function of the predicted porosity positions, laser process parameters in the next layer can be adjusted to avoid more part porosity in the future or the existing porosity could be filled. If the predicted part porosity is not acceptable regardless of laser parameters, the building process can be stopped to minimize the loss.

Original languageEnglish (US)
Pages (from-to)315-329
Number of pages15
JournalJournal of Intelligent Manufacturing
Volume34
Issue number1
DOIs
StatePublished - Jan 2023

Keywords

  • Additive manufacturing
  • Convolutional neural network
  • Encoder–decoder
  • Porosity prediction
  • Powder bed fusion
  • Thermal signatures

ASJC Scopus subject areas

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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