TY - CONF A1 - Maierhofer, Christiane A1 - Thiel, Erik A1 - Altenburg, Simon A1 - Mohr, Gunther A1 - Thiede, Tobias A1 - Mishurova, Tatiana A1 - Paul, Andrea A1 - Kranzmann, Axel A1 - Hilgenberg, Kai A1 - Pittner, Andreas A1 - Bruno, Giovanni A1 - Sommer, Konstantin A1 - Gumenyuk, Andrey T1 - Quality control in additive manufacturing via in-situ monitoring and non-destructive testing N2 - More than 80 representatives of SMEs, industrial companies and research institutes met on September 12 at the workshop "Challenges in Additive Manufacturing: Innovative Materials and Quality Control" at BAM in Adlershof to discuss the latest developments in materials and quality control in additive manufacturing. In special lectures, researchers, users and equipment manufacturers reported on the latest and future developments in additive manufacturing. Furthermore, funding opportunities for projects between SMEs and research institutions on a national and European level were presented. T2 - Challenges in Additive Manufacturing: Innovative Materials and Quality Control CY - Berlin, Germany DA - 12.09.2018 KW - Additive manufacturing KW - Quality control KW - Non-destructive testing KW - In-situ monitoring PY - 2018 AN - OPUS4-46072 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 - 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 - 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 U6 - 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 - Pelkner, Matthias A1 - Ehlers, Henrik A1 - Thewes, R. T1 - Online Eddy Current Testing of PBF LB/M Parts Using GMR Sensor Arrays During Manufacturing N2 - In recent years, additive manufacturing technologies have gained in importance. Laser powder bed fusion can be used for complex functional components or the production of workpieces in small quantities. High safety requirements, e.g. in aerospace, demand comprehensive quality control. Therefore, non-destructive offline inspection methods such as computed tomography are used after production. Recently, online non-destructive testing methods such as optical tomography have been developed to improve profitability and practicality. In this presentation, the applicability of eddy current inspection using GMR sensors for online inspection of PBF-LB/M parts is demonstrated. Eddy current testing is performed for each layer during the production process at frequencies uo to 1.2 MHz. Despite the use of high-resolution arrays with 128 elements, the testing time is kept low by an adapted hardware. Thus, the measurement can be performed during the manufacturing process without significantly slowing down the production process. In addition to the approach, the results of an online eddy current test of a step-shaped test specimen made of Haynes282 are presented. T2 - European Conference on Non-Destructive Testing (ECNDT) 2023 CY - Lisbon, Potugal DA - 03.07.2023 KW - Eddy current KW - GMR KW - Additive manufacturing KW - In-situ monitoring KW - NDT PY - 2023 AN - OPUS4-58082 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -