TY - JOUR A1 - Oster, Simon A1 - Breese, Philipp Peter A1 - Ulbricht, Alexander A1 - Mohr, Gunther A1 - Altenburg, Simon T1 - A deep learning framework for defect prediction based on thermographic in-situmonitoring in laser powder bed fusion N2 - 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. Themodel 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. KW - Laser Powder Bed Fusion (PBF-LB/M, L-PBF) KW - Selective Laser Melting (SLM) KW - SWIR thermography KW - Online monitoring KW - Flaw detection KW - Machine learning KW - Convolutional neural networks (CNN) PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-575148 DO - https://doi.org/10.1007/s10845-023-02117-0 SN - 0956-5515 SP - 1 EP - 20 PB - Springer AN - OPUS4-57514 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mohr, Gunther A1 - Altenburg, Simon A1 - Hilgenberg, Kai T1 - On the limitations of small cubes as test coupons for process parameter optimization in laser powder bed fusion of metals N2 - The capability to produce complexly and individually shaped metallic parts is one of the main advantages of the laser powder bed fusion (PBF LB/M) process. Development of material and machine specific process parameters is commonly based on results acquired from small cubic test coupons of about 10 mm edge length. Such cubes are usually used to conduct an optimization of process parameters to produce dense material. The parameters are then taken as the basis for the manufacturing of real part geometries. However, complex geometries go along with complex thermal histories during the manufacturing process, which can significantly differ from thermal conditions prevalent during the production of simply shaped test coupons. This may lead to unexpected and unpredicted local inhomogeneities of the microstructure and defect distribution in the final part and it is a root cause of reservations against the use of additive manufacturing for the production of safety relevant parts. In this study, the influence of changing thermal conditions on the resulting melt pool depth of 316L stainless steel specimens is demonstrated. A variation of thermo-graphically measured intrinsic preheating temperatures was triggered by an alteration of inter layer times and a variation of cross section areas of specimens for three distinct sets of process parameters. Correlations between the preheating temperature, the melt pool depth, and occurring defects were analyzed. The limited expressiveness of the results of small density cubes is revealed throughout the systematic investigation. Finally, a clear recommendation to consider thermal conditions in future process parameter optimizations is given. T2 - Icaleo 2023 CY - Chicago, IL, USA DA - 16.10.2023 KW - Additive manufacturing KW - Heat accumulation KW - Thermal history KW - In situ monitoring KW - Representative specimens KW - Thermography PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-586558 DO - https://doi.org/10.2351/7.0001080 SN - 1042-346X VL - 35 IS - 4 SP - 1 EP - 10 AN - OPUS4-58655 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zocca, Andrea A1 - Lüchtenborg, Jörg A1 - Mühler, T. A1 - Wilbig, Janka A1 - Mohr, Gunther A1 - Villatte, T. A1 - Léonard, Fabien A1 - Nolze, Gert A1 - Sparenberg, M. A1 - Melcher, J. A1 - Hilgenberg, Kai A1 - Günster, Jens T1 - Enabling the 3D Printing of Metal Components in μ-Gravity N2 - As humanity contemplates manned missions to Mars, strategies need to be developed for the design and operation of hospitable environments to safely work in space for years. The supply of spare parts for repair and replacement of lost equipment will be one key need, but in-space manufacturing remains the only option for a timely supply. With high flexibility in design and the ability to manufacture ready-to-use components directly from a computeraided model, additive manufacturing (AM) technologies appear extremely attractive. For the manufacturing of metal parts, laser-beam melting is the most widely used AM process. However, the handling of metal powders in the absence of gravity is one prerequisite for its successful application in space. A gas flow throughout the powder bed is successfully applied to compensate for missing gravitational forces in microgravity experiments. This so-called gas-flow-assisted powder deposition is based on a porous Building platform acting as a filter for the fixation of metal particles in a gas flow driven by a pressure difference maintained by a vacuum pump. KW - Additive manufacturing KW - µ-gravity KW - Laser beam melting KW - Parabolic flight KW - 3D printing PY - 2019 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-492190 DO - https://doi.org/10.1002/admt.201900506 SP - 1900506 PB - WILEY-VCH Verlag GmbH AN - OPUS4-49219 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mohr, Gunther A1 - Scheuschner, Nils A1 - Hilgenberg, Kai T1 - In situ heat accumulation by geometrical features obstructing heat flux and by reduced inter layer times in laser powder bed fusion of AISI 316L stainless steel N2 - Material qualification for laser powder bed fusion (L-PBF) processes are often based on results derived from additively manufactured (AM) bulk material or small density cubes, although it is well known that the part geometry has a tremendous influence on the heat flux and, therefore, on the thermal history of an AM component. This study shows experimentally the effect of simple geometrical obstructions to the heat flux on cooling behavior and solidification conditions of 316L stainless steel processed by L-PBF. Additionally, it respects two distinct inter layer times (ILT) as well as the build height of the parts. The cooling behavior of the parts is in-situ traced by infrared (IR) thermography during the built-up. The IR signals reveal significant differences in cooling conditions, which are correlated to differences in melt pool geometries. The acquired data and results can be used for validation of computational models and improvements of quality assurance. KW - Selective laser beam melting KW - Thermography KW - Melt pool depth KW - Inter layer time KW - Ppreheating temperature KW - Additive Manufacturing PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-512549 DO - https://doi.org/10.1016/j.procir.2020.09.030 VL - 94 SP - 155 EP - 160 PB - Elsevier B.V. AN - OPUS4-51254 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Portella, Pedro Dolabella A1 - Ulbricht, Alexander A1 - Evans, Alexander A1 - Altenburg, Simon A1 - Ehlers, Henrik A1 - Hilgenberg, Kai A1 - Mohr, Gunther T1 - Monitoring additive manufacturing processes by using NDT methods N2 - In this presentation we discuss the online monitoring of metallic AM parts produced by the Laser Powder Bed Fusion (LPBF) process by using optical, thermographic and electromagnetic methods. In a first approach we present the detection of defects generated during the process and discuss how to improve these methods for the optimization of design and production of metallic AM parts. T2 - ABENDI - Workshop CY - Online meeting DA - 19.11.2020 KW - Additive Fertigung PY - 2020 AN - OPUS4-52042 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Sonntag, Nadja A1 - Piesker, Benjamin A1 - Ávila Calderón, Luis Alexander A1 - Mohr, Gunther A1 - Rehmer, Birgit A1 - Agudo Jácome, Leonardo A1 - Hilgenberg, Kai A1 - Evans, Alexander A1 - Skrotzki, Birgit T1 - Tensile and Low‐Cycle Fatigue Behavior of Laser Powder Bed Fused Inconel 718 at Room and High Temperature N2 - This study investigates the room‐ and high‐temperature (650 °C) tensile and low‐cycle‐fatigue behavior of Inconel 718 produced by laser powder bed fusion (PBF‐LB/M) with a four‐step heat treatment and compares the results to the conventional wrought material. The microstructure after heat treatment is characterized on different length scales. Compared to the wrought variant, the elastic and yield properties are comparable at both test temperatures while tensile strength, ductility, and strain hardening capacity are lower. The fatigue life of the PBF‐LB/M variant at room temperature is slightly lower than that of the wrought material, while at 650 °C, it is vice versa. The cyclic stress response for both material variants is characterized by cyclic softening, which is more pronounced at the higher test temperature. High strain amplitudes (≥0.7%) at room temperature and especially a high testing temperature result in the formation of multiple secondary cracks at the transitions of regions comprising predominantly elongated grain morphology and columns of stacked grains with ripple patterns in the PBF‐LB/M material. This observation and pronounced crack branching and deflection indicate that the cracks are controlled by sharp micromechanical gradients and local crystallite clusters. KW - Additive manufacturing KW - Fatigue damage KW - Heat treatment KW - Inconel 718 KW - Laser powder bed fusion KW - Low-cycle fatigue KW - Tensile strength PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-599316 DO - https://doi.org/10.1002/adem.202302122 SN - 1527-2648 SP - 1 EP - 17 PB - Wiley AN - OPUS4-59931 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CHAP A1 - Maierhofer, Christiane A1 - Altenburg, Simon A1 - Scheuschner, Nils ED - Meyendorf, N. ED - Ida, N. ED - Singh, R. ED - Vrana, J. T1 - In Situ Real-Time Monitoring Versus Post NDE for Quality Assurance of Additively Manufactured Metal Parts N2 - In this chapter, the current state-of-the-art of in situ monitoring and in situ NDE methods in additive manufacturing is summarized. The focus is set on methods, which are suitable for making statements about the quality and usability of a component currently being manufactured. This includes methods which can be used to determine state properties like temperature or density, other physical properties like electrical or thermal conductivity, the microstructure, the chemical composition, the actual geometry, or which enable the direct detection of defects like cracks, voids, delaminations, or inclusions. Thus, optical, thermographic, acoustic, and electromagnetic methods, as well as methods being suitable for investigating particle and fume emission are presented. The requirements of in situ monitoring methods with a focus on thermographic methods are discussed by considering different additive manufacturing processes like laser powder bed fusion (PBF-LB/M) and direct energy deposition (DED-LB/M). Examples of the successful implementation and applications of such monitoring methods at BAM are given. The in situ monitoring and NDE methods are compared against post-process NDE methods. The advantages and challenges of in situ methods concerning real-time data analysis and the application of AI algorithms are addressed and discussed. KW - Additive manufacturing KW - In situ monitoring KW - In situ NDE KW - Post NDE KW - Thermography KW - Laser powder bed fusion KW - Direct energy deposition PY - 2021 SN - 978-3-030-48200-8 DO - https://doi.org/10.1007/978-3-030-48200-8_51-1 SP - 1 EP - 37 PB - Springer Nature Switzerland AG CY - Cham, Switzerland ET - 1 AN - OPUS4-52824 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Oster, Simon A1 - Scheuschner, Nils A1 - Chand, Keerthana A1 - Altenburg, Simon A1 - Gerlach, G. T1 - Potentials and challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring in laser powder bed fusion N2 - Laser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled. Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring. Therefore, the porosity prediction task is studied in detail using an exemplary dataset from the manufacturing of two Haynes282 cuboid components. Our trained 1D convolutional neural network model shows high performance (R² score of 0.90) for the prediction of local porosity in discrete sub-volumes with dimensions of (700 x 700 x 40) μm³. It could be demonstrated that the regressor correctly predicts layer-wise porosity changes but presumably has limited capability to predict differences in local porosity. Furthermore, there is a need to study the significance of the used thermogram feature inputs to streamline the model and to adjust the monitoring hardware. Moreover, we identified multiple sources of data uncertainty resulting from the in-situ monitoring setup, the registration with the ground truth X-ray-computed tomography data and the used pre-processing workflow that might influence the model’s performance detrimentally. T2 - XXXVII. Messtechnisches Symposium 2023 CY - Freiburg, Germany DA - 27.09.2023 KW - Porosity prediction KW - Defect detection KW - Laser powder bed fusion (PBF-LB/M, L-PBF) KW - Selective laser melting KW - Thermography KW - Machine learning PY - 2023 DO - https://doi.org/10.1515/teme-2023-0062 SN - 0171-8096 SN - 2196-7113 VL - 90 SP - 85 EP - 96 PB - De Gruyter CY - Berlin AN - OPUS4-58366 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Altenburg, Simon A1 - Mohr, Gunther A1 - Baesso, Ilaria A1 - Straße, Anne A1 - Pittner, Andreas A1 - Pignatelli, Giuseppe A1 - Seeger, Stefan A1 - Nazarzadehmoafi, Maryam A1 - Ehlers, Henrik A1 - Gohlke, Dirk A1 - Homann, Tobias A1 - Scheuschner, Nils A1 - Ulbricht, Alexander A1 - Heinrich, P. A1 - Maierhofer, Christiane T1 - Process monitoring of additive manufacturing of metals - an overview of the project ProMoAM N2 - The project ProMoAM is presented. The goal of the project is to evaluate which NDT techniques or combination of techniques is suited for in-situ quality assurance in additive manufacturing of metals. To this end, also 3d-data fusion and visualization techniques are applied. Additional ex-situ NDT-techniques are used as references for defect detection and quantification. Feasability studies for NDT-techniques that are presently not applicable for in-situ use are performed as well. The presentation gives a brief overview of the whole project and the different involved NDT-techniques. T2 - Workshop od Additive Manufacturing: Process, materials, simulation & implants CY - Berlin, Germany DA - 13.05.2019 KW - Additive manufacturing KW - Process monitoring KW - NDT PY - 2019 AN - OPUS4-48087 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Evans, Alexander A1 - Ávila, Luis A1 - Werner, Tiago A1 - Sommer, Konstantin A1 - Ulbricht, Alexander A1 - Sprengel, Maximilian A1 - Charmi, Amir A1 - Mohr, Gunther A1 - Rehmer, Birgit A1 - Skrotzki, Birgit A1 - Falkenberg, Rainer A1 - Bettge, Dirk A1 - Madia, Mauro A1 - Zerbst, Uwe A1 - Kromm, Arne A1 - Hilgenberg, Kai A1 - Kannengießer, Thomas A1 - Bruno, Giovanni T1 - Ageing behaviour of laser powder bed fused 316L: a powder to failure approach N2 - Laser powder bed fusion (LPBF) is an additive manufacturing process for materials which inherently tends to yield various degrees of metastable hierarchical microstructures, defects and high residual stresses in the as-built condition depending on the process parameters. The understanding of the evolution of these typical features during heat treatment and subsequent thermal and mechanical ageing is crucial for the wider acceptance for safety critical structures. A multi-disciplinary research project at BAM studying the development of the microstructure, defects, residual stresses typical of LPBF 316L and their evolution during thermal and mechanical ageing has led to insights into the stability of these inherent features. This presentation aims to give a broad overview of the project with a few specific cases of investigation. Firstly, the formation of residual stresses, the nature of the initial microstructure, the tensile properties and a modelling approach to understand the anisotropy will be presented. This will be followed by examples of studies of their evolution during heat treatment, long term thermal exposure, and room temperature and high temperature mechanical testing compared to a baseline of conventional wrought variant of the same alloy. T2 - International Conference on Additive Manufacturing 2021 (ICAM 2021) CY - Online meeting DA - 01.11.2021 KW - Ageing KW - Additive manufacturing KW - Laser powder bed fusion KW - AGIL PY - 2021 AN - OPUS4-54106 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -