TY - CONF A1 - Hilgenberg, Kai T1 - Additive Manufacturing: Opportunities and challenges for NDT N2 - Additive manufacturing processes are increasingly being used in industrial applications. Especially powder bed fusion processes are of high interest due to their capability to economically produce individual, highly complex and functionally integrated components in small batches. However, the quality assurance of these components remains a challenge. Internal defects and undesirable microstructures and surface conditions can deteriorate the mechanical properties. Especially for use in safety-relevant applications, new design and inspection concepts are needed that take these factors into account. This talk presents typical defects and microstructure phenomena resulting from the laser powder bed fusion process and identifies challenges and opportunities for non-destructive testing from a manufacturing engineering perspective. In particular, the possibility of a process-integrated quality control is shown based on current research results. T2 - The 13th International Symposium on NDT in Aerospace 2021 CY - Online meeting DA - 05.10.2021 KW - Additive manufacturing KW - Laser powder bed fusion KW - In-situ monitoring PY - 2021 AN - OPUS4-53484 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Maierhofer, Christiane A1 - Scheuschner, Nils A1 - Altenburg, Simon T1 - Summary of in-situ monitoring in additive manufacturing - ProMoAM N2 - The quality of additively manufactured components is significantly influenced by the process parameters used during production. Thus, sensors and measuring systems are already commercially available for process monitoring, at least in metal-based additive manufacturing. However, it is not yet possible to detect defects and inhomogeneities directly or indirectly during the building process. The aim of the project ProMoAM is to develop spectroscopic and non-destructive testing methods for the in-situ evaluation of the quality of additively manufactured metal components in laser- or arc-based AM processes. In addition to passive and active methods of thermography, this includes optical tomography, optical emission spectroscopy, eddy current testing, laminography (radiography), X-ray backscattering, particle emission spectroscopy and photoacoustic methods. T2 - 2nd Workshop on In-situ Monitoring and Microstructure Development in Additive Manufactured Alloys CY - Online meeting DA - 19.04.2021 KW - In-situ monitoring KW - Additive manufacturing KW - Metals KW - Thermography PY - 2021 AN - OPUS4-52539 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Thiel, Erik A1 - Altenburg, Simon A1 - Myrach, P. A1 - Mohr, Gunther A1 - Gumenyuk, Andrey T1 - Crack detection in metal additive manufacturing with laser thermography N2 - BAM ensures and represents high standards for safety in technology and chemistry. Additive manufacturing (AM) changes the requirements for conventional non-destructive testing (NDT) as new processes of defect creation occur. Especially in safety critical areas, such as aerospace and automotive, new manufacturing processes and materials always require reliability tests and new standards which is a big challenge for NDT. T2 - Internationales Laser- und Fügesymposium CY - Dresden, Germany DA - 27.02.2018 KW - Laser Beam Melting KW - Laser Metal Deposition KW - Thermography KW - In-situ monitoring PY - 2018 AN - OPUS4-45619 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon A1 - Scheuschner, Nils A1 - Chand, Keerthana A1 - Breese, Philipp Peter A1 - Becker, Tina A1 - Heinrichsdorff, F. A1 - Altenburg, Simon T1 - From Thermographic In-situ Monitoring to Porosity Detection – A Deep Learning Framework for Quality Control in Laser Powder Bed Fusion T2 - SMSI - Sensor and Measurement Science International - Proceedings N2 - In this study, we present an enhanced deep learning framework for the prediction of porosity based on thermographic in-situ monitoring data of laser powder bed fusion processes. The manufacturing of two cuboid specimens from Haynes 282 (Ni-based alloy) powder was monitored by a short-wave infrared camera. We use thermogram feature data and x-ray computed tomography data to train a convolutional neural network classifier. The classifier is used to perform a multi-class prediction of the spatially resolved porosity level in small sub-volumes of the specimen bulk. T2 - SMSI - Sensor and Measurement Science International 2023 CY - Nürnberg, Germany DA - 08.05.2023 KW - Porosity KW - Laser powder bed fusion KW - In-situ monitoring KW - Thermography KW - Machine Learning PY - 2023 UR - https://www.ama-science.org/proceedings/details/4404 DO - https://doi.org/10.5162/SMSI2023/C5.4 SP - 179 EP - 180 AN - OPUS4-57616 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Thiel, Erik A1 - Altenburg, Simon A1 - Mohr, Gunther A1 - Thiede, Tobias A1 - Maierhofer, Christiane A1 - Bruno, Giovanni A1 - Rethmeier, Michael A1 - Hilgenberg, Kai A1 - Mishurova, Tatiana A1 - Straße, Anne T1 - AM activities at BAM with focus on process monitoring N2 - The presentation gives an overview of current projects in additive manufacturing at BAM. In particular, the results of the ProMoAm project were presented. T2 - VAMAS - Materials Issues in Additive Manufacturing CY - Berlin, Germany DA - 25.06.2018 KW - Additive Manufacturing KW - Laser Metal Deposition KW - Thermography KW - Data Fusion KW - In-situ monitoring PY - 2018 AN - OPUS4-45620 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon A1 - Maierhofer, Christiane A1 - Mohr, Gunther A1 - Hilgenberg, Kai A1 - Ulbricht, Alexander A1 - Altenburg, Simon T1 - Investigation of the thermal history of L-PBF metal parts by feature extraction from in-situ SWIR thermography N2 - Laser powder bed fusion is used to create near net shape metal parts with a high degree of freedom in geometry design. When it comes to the production of safety critical components, a strict quality assurance is mandatory. An alternative to cost-intensive non-destructive testing of the produced parts is the utilization of in-situ process monitoring techniques. The formation of defects is linked to deviations of the local thermal history of the part from standard conditions. Therefore, one of the most promising monitoring techniques in additive manufacturing is thermography. In this study, features extracted from thermographic data are utilized to investigate the thermal history of cylindrical metal parts. The influence of process parameters, part geometry and scan strategy on the local heat distribution and on the resulting part porosity are presented. The suitability of the extracted features for in-situ process monitoring is discussed. T2 - Thermosense: Thermal Infrared Applications XLIII CY - Online meeting DA - 12.04.2021 KW - SWIR camera KW - Additive manufacturing (AM) KW - Selective laser melting (SLM) KW - Laser beam melting (LBM) KW - In-situ monitoring KW - Infrared thermography PY - 2021 SN - 978-1-5106-4324-6 DO - https://doi.org/10.1117/12.2587913 VL - 11743 SP - 1 EP - 11 PB - SPIE - The international society for optics and photonics AN - OPUS4-52535 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Becker, Tina A1 - Altenburg, Simon A1 - Scheuschner, Nils A1 - Maierhofer, Christiane T1 - Multi measurand in-situ monitoring of the laser powder bed fusion process by means of multispectral optical tomography N2 - Laser Powder Bed Fusion (L-PBF), as one of the most promising production process in the field of metal additive manufacturing, enables traditional constructive solutions to be rethought and the manufacturing of optimized components according to the "form follows function" principle. The most significant obstacle for a broad industrial application of the L-PBF process is the inadequate quality assurance during the manufacturing process so far, leading to high production costs. Although several mainly camera based commercial in-process monitoring systems are already available, a deep understanding of the interpretation of the monitored data and correlation with actual defects is still lacking. One reason for this is the reduction of the complex process signature to just one measurement value. The focus of this contribution is the presentation of the multispectral optical tomography as alternative to single measurand in-situ monitoring systems. The potential of this approach is hereby shown on L-PBF printed samples with induced process instabilities. Beyond that, an in-house developed L-PBF printer for further testing of multi-sensor in-situ monitoring systems is presented. T2 - ICAM2021 CY - Online meeting DA - 01.11.2021 KW - In-situ monitoring KW - L-PBF KW - Optical tomography KW - 3d printing KW - Thermography PY - 2021 AN - OPUS4-54388 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon A1 - Scheuschner, Nils A1 - Chand, Keerthana A1 - Breese, Philipp Peter A1 - Becker, Tina A1 - Heinrichsdorff, F. A1 - Altenburg, Simon T1 - From Thermographic In-situ Monitoring to Porosity Detection – A Deep Learning Framework for Quality Control in Laser Powder Bed Fusion N2 - In this study, we present an enhanced deep learning framework for the prediction of porosity based on thermographic in-situ monitoring data of laser powder bed fusion processes. The manufacturing of two cuboid specimens from Haynes 282 (Ni-based alloy) powder was monitored by a short-wave infrared camera. We use thermogram feature data and x-ray computed tomography data to train a convolutional neural network classifier. The classifier is used to perform a multi-class prediction of the spatially resolved porosity level in small sub-volumes of the specimen bulk. T2 - Sensor and Measurement Science International CY - Nurnberg, Germany DA - 08.05.2023 KW - Laser powder bed fusion KW - In-situ monitoring KW - Thermography KW - Machine Learning KW - Porosity PY - 2023 AN - OPUS4-57614 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon A1 - Maierhofer, Christiane A1 - Mohr, Gunther A1 - Hilgenberg, Kai A1 - Ulbricht, Alexander A1 - Altenburg, Simon T1 - Investigation of the thermal history of L-PBF metal parts by feature extraction from in-situ SWIR thermography N2 - Laser powder bed fusion is used to create near net shape metal parts with a high degree of freedom in geometry design. When it comes to the production of safety critical components, a strict quality assurance is mandatory. An alternative to cost-intensive non-destructive testing of the produced parts is the utilization of in-situ process monitoring techniques. The formation of defects is linked to deviations of the local thermal history of the part from standard conditions. Therefore, one of the most promising monitoring techniques in additive manufacturing is thermography. In this study, features extracted from thermographic data are utilized to investigate the thermal history of cylindrical metal parts. The influence of process parameters, part geometry and scan strategy on the local heat distribution and on the resulting part porosity are presented. The suitability of the extracted features for in-situ process monitoring is discussed. T2 - Thermosense: Thermal Infrared Applications XLIII CY - Online meeting DA - 12.04.2021 KW - SWIR camera KW - Additive manufacturing (AM) KW - Selective laser melting (SLM) KW - Laser beam melting (LBM) KW - In-situ monitoring KW - Infrared thermography PY - 2021 UR - https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11743/117430C/Investigation-of-the-thermal-history-of-L-PBF-metal-parts/10.1117/12.2587913.short?SSO=1&tab=ArticleLink AN - OPUS4-52540 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -