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Article

In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography

by
Gunther Mohr
1,2,*,
Simon J. Altenburg
2,
Alexander Ulbricht
2,
Philipp Heinrich
3,
Daniel Baum
3,
Christiane Maierhofer
2 and
Kai Hilgenberg
1,2
1
Institute of Machine Tools and Factory Management, Chair of Processes and Technologies for Highly Loaded Welds, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany
2
Federal Institute for Materials Research and Testing (BAM; Bundesanstalt für Materialforschung und prüfung), Unter den Eichen 87, 12205 Berlin, Germany
3
Konrad-Zuse-Zentrum für Informationstechnik Berlin (ZIB), Mathematics for Life and Materials Sciences Takustraße 7, 14195 Berlin, Germany
*
Author to whom correspondence should be addressed.
Metals 2020, 10(1), 103; https://doi.org/10.3390/met10010103
Submission received: 24 November 2019 / Revised: 13 December 2019 / Accepted: 20 December 2019 / Published: 9 January 2020
(This article belongs to the Special Issue Advanced Characterization and On-Line Process Monitoring of Additively Manufactured Materials and Components)
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Abstract

Among additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data integration are presented.
Keywords: laser powder bed fusion (L-PBF); selective laser melting (SLM); additive manufacturing (AM); process monitoring; infrared thermography; optical tomography; computed tomography (CT); data fusion; lack-of-fusion laser powder bed fusion (L-PBF); selective laser melting (SLM); additive manufacturing (AM); process monitoring; infrared thermography; optical tomography; computed tomography (CT); data fusion; lack-of-fusion

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MDPI and ACS Style

Mohr, G.; Altenburg, S.J.; Ulbricht, A.; Heinrich, P.; Baum, D.; Maierhofer, C.; Hilgenberg, K. In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography. Metals 2020, 10, 103. https://doi.org/10.3390/met10010103

AMA Style

Mohr G, Altenburg SJ, Ulbricht A, Heinrich P, Baum D, Maierhofer C, Hilgenberg K. In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography. Metals. 2020; 10(1):103. https://doi.org/10.3390/met10010103

Chicago/Turabian Style

Mohr, Gunther, Simon J. Altenburg, Alexander Ulbricht, Philipp Heinrich, Daniel Baum, Christiane Maierhofer, and Kai Hilgenberg. 2020. "In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography" Metals 10, no. 1: 103. https://doi.org/10.3390/met10010103

APA Style

Mohr, G., Altenburg, S. J., Ulbricht, A., Heinrich, P., Baum, D., Maierhofer, C., & Hilgenberg, K. (2020). In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography. Metals, 10(1), 103. https://doi.org/10.3390/met10010103

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