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 and Ankit Agrawal ()
Additional contact information
Yuwei Mao: Northwestern University
Hui Lin: Northwestern University
Christina Xuan Yu: Sigma Labs, Inc.
Roger Frye: Sigma Labs, Inc.
Darren Beckett: Sigma Labs, Inc.
Kevin Anderson: Sigma Labs, Inc.
Lars Jacquemetton: Sigma Labs, Inc.
Fred Carter: Northwestern University
Zhangyuan Gao: Northwestern University
Wei-keng Liao: Northwestern University
Alok N. Choudhary: Northwestern University
Kornel Ehmann: Northwestern University
Ankit Agrawal: Northwestern University

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 1, No 15, 315-329

Abstract: 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.

Keywords: Porosity prediction; Thermal signatures; Convolutional neural network; Encoder–decoder; Powder bed fusion; Additive manufacturing (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-022-02039-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:34:y:2023:i:1:d:10.1007_s10845-022-02039-3

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-022-02039-3

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().