A deep learning framework for defect prediction based on thermographic in-situ monitoring in laser powder bed fusion

Simon Oster (), Philipp P. Breese, Alexander Ulbricht, Gunther Mohr and Simon J. Altenburg
Additional contact information
Simon Oster: Bundesanstalt für Materialforschung und-prüfung (BAM, Federal Institute for Materials Research and Testing)
Philipp P. Breese: Bundesanstalt für Materialforschung und-prüfung (BAM, Federal Institute for Materials Research and Testing)
Alexander Ulbricht: Bundesanstalt für Materialforschung und-prüfung (BAM, Federal Institute for Materials Research and Testing)
Gunther Mohr: Bundesanstalt für Materialforschung und-prüfung (BAM, Federal Institute for Materials Research and Testing)
Simon J. Altenburg: Bundesanstalt für Materialforschung und-prüfung (BAM, Federal Institute for Materials Research and Testing)

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 4, No 14, 1687-1706

Abstract: Abstract The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers. Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. The model is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) µm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity.

Keywords: Laser Powder Bed Fusion (PBF-LB/M; L-PBF); Selective Laser Melting (SLM); SWIR thermography; Online monitoring; Flaw detection; Machine learning; Convolutional neural networks (CNN) (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02117-0 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:35:y:2024:i:4:d:10.1007_s10845-023-02117-0

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

DOI: 10.1007/s10845-023-02117-0

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 ().