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

Metals

Daniel Baum¹  Alexander Ulbricht¹  Philipp Heinrich²  SimonJ. Altenburg²  Gunther Mohr³  Kai Hilgenberg⁴  Christiane Maierhofer⁴ 

[1] Bundesanstalt für Materialforschung und prüfung), Unter den Eichen 87, 12205 Berlin, Germany;Federal Institute for Materials Research and Testing (BAM;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;Konrad-Zuse-Zentrum für Informationstechnik Berlin (ZIB), Mathematics for Life and Materials Sciences Takustraße 7, 14195 Berlin, Germany;

关键词: 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;   

DOI  :  10.3390/met10010103

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown