TY - CONF A1 - Mishurova, Tatiana T1 - 3D imaging and residual stress analysis of AM materials at BAM N2 - The overview of the activity of group 8.5 Micro-NDT (BAM, Belin, Germany) in the field of additively manufacturing material characterization will be presented. The research of our group is focused on the 3D imaging of AM materials by means of X-ray Computed Tomography at the lab and at synchrotron, and the residual stress characterization by diffraction (nondestructive technique). T2 - Seminar at Chalmers University and Centre for Additive Manufacture (CAM2) CY - Gothenburg, Sweden DA - 19.05.2022 KW - Additive manufacturing KW - Laser powder bed fusion KW - Residual stress KW - Computed tomography KW - Synchrotron X-ray diffraction KW - X-ray refraction PY - 2022 AN - OPUS4-55019 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Roveda, Ilaria T1 - Investigation of residual stresses and microstructure effects on the fatigue behaviour of a L-PBF AlSi10Mg alloy N2 - Laser Powder Bed Fusion (L-PBF) allow the fabrication of lightweight near net shape AlSi10Mg components attractive to the aerospace, automotive, biomedical and military industries. During the build-up process, high cooling rates occur. Thus, L-PBF AlSi10Mg alloys exhibit a Si-nanostructure in the as-built condition, which leads to superior mechanical properties compared to conventional cast materials. At the same time, such high thermal gradients generally involve a deleterious residual stress (RS) state that needs to be assessed during the design process, before placing a component in service. To this purpose post-process heat treatments are commonly performed to relieve detrimental RS. In this contribution two low-temperature stress-relief heat treatments (SRHT) are studied and compared with the as-built state: a SRHT at 265°C for 1 hour and a SRHT at 300°C for 2 hours. At these temperatures microstructural changes occur. In the as-built state, Si atoms are supersaturated in the α-aluminium matrix, which is enveloped by a eutectic Si-network. At 265°C the Si precipitation from the matrix to the pre-existing network is triggered. Thereafter, above 295°C the fragmentation and spheroidization of the Si branches takes place, presumably by Al–Si interdiffusion. After 2 hours the original eutectic network is completely replaced by uniformly distributed blocky particles. The effect of the heat and the microstructure modification on the RS state and the fatigue properties is investigated. Energy dispersive x-ray and neutron diffraction are combined to investigate the near-surface and bulk RS state of a L-PBF AlSi10Mg material. Differences in the endurance limit are evaluated experimentally by high cycle fatigue (HCF) tests and cyclic R-curve determination. T2 - 43rd Materials Mechanics Seminar CY - Sani, Chalkidiki, Greece DA - 05.06.2022 KW - AlSi10Mg KW - Fatigue KW - Neutron diffraction KW - X-ray diffraction PY - 2022 AN - OPUS4-55090 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Gupta, P. A1 - Karnaushenko, D. D. A1 - Becker, C. A1 - Okur, I. E. A1 - Melzer, Michael A1 - Özer, B. A1 - Schmidt, O. G. A1 - Karnaushenko, D. T1 - Large Scale Exchange Coupled Metallic Multilayers by Roll-to-Roll (R2R) Process for Advanced Printed Magnetoelectronics N2 - Till now application of printed magnetoelectronics is hindered by lack of large area exchange coupled metallic multilayers required to produce printable magneto-sensory inks. Large-scale roll-to-roll (R2R) fabrication process is an attractive approach owing to its capabilities for high volume, high throughput, and large area manufacturing. Precise and high performance R2R sputtering technology is developed to fabricate large area giant magnetoresistive (GMR) thin-films stacks that contain 30 metallic bilayers prepared by continuous R2R sputtering of Co and Cu sequential on a hundred meters long polyethylene terephthalate (PET) web. The R2R sputtered Co/Cu multilayer on a 0.2 × 100 m2 PET web exhibits a GMR ratio of ≈40% achieving the largest area exchange coupled room temperature magneto-sensitive system demonstrated to date. The prepared GMR thin-film is converted to magnetosensitive ink that enables printing of magnetic sensors with high performance in a cost-efficient way, which promotes integration with printed electronics. An average GMR ratio of ≈18% is obtained for 370 printed magnetic sensors. The realized precise R2R sputtering approach can also be extended to a wide range of hybrid thin-film material systems opening up a path for new functional inks applied with printing technologies. KW - Printed Electronics KW - Flexible Magnetic Sensors KW - Roll-to-Roll Processing KW - Functional Materials KW - Upscaling PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-552344 DO - https://doi.org/10.1002/admt.202200190 SN - 2365-709X SP - 1 EP - 11 PB - Wiley-VCH CY - Weinheim, Deutschland AN - OPUS4-55234 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Scheuschner, Nils T1 - In-situ Monitoring of PBF-LB/M by thermography, optical tomography, melt-pool-monitoring and eddy current N2 - The formation of defects such as keyhole pores is a major challenge for the production of metal parts by Laser Powder Bed Fusion (LPBF). The LPBF process is characterized by a large number of influencing factors which can be hard to quantify. Machine Learning (ML) is a prominent tool to predict the outcome of complex processes on the basis of different sensor data. In this study, a ML model for defect prediction is created using thermographic image features as input data. As a reference, the porosity information calculated from an x-ray Micro Computed Tomography (µCT) scan of the produced specimen is used. Physical knowledge about the keyhole pore formation is incorporated into the model to increase the prediction accuracy. From the prediction result, the quality of the input data is evaluated and future demands on in-situ monitoring of LPBF processes are formulated. T2 - AM Bench 2022 CY - Bethesda, Washingthon DC, USA DA - 15.08.2022 KW - Additive Manufacturing KW - Thermography KW - Additive Fertigung KW - Thermografie PY - 2022 AN - OPUS4-55854 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Schumacher, David A1 - Waske, Anja T1 - XCT data of metallic feedstock powder with pore size analysis N2 - X-Ray computed tomography (XCT) scan of 11 individual metallic powder particles, made of (Mn,Fe)2(P,Si) alloy. The data set consists of 4 single XCT scans which have been stitched together [3] after reconstruction. The powder material is an (Mn,Fe)2(P,Si) alloy with an average density of 6.4 g/cm³. The particle size range is about 100 - 150 µm with equivalent pore diameters up to 75 µm. The powder and the metallic alloy are described in detail in [1, 2]. KW - Additive Manufacturing KW - Feedstock powder KW - Powder Characterization KW - X-Ray Computed Tomography PY - 2022 DO - https://doi.org/10.5281/zenodo.5796487 PB - Zenodo CY - Geneva AN - OPUS4-55556 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - D'Accardi, E. A1 - Krankenhagen, Rainer A1 - Ulbricht, Alexander A1 - Pelkner, Matthias A1 - Pohl, Rainer A1 - Palumbo, D. A1 - Galietti, U. T1 - Capability to detect and localize typical defects of laser powder bed fusion (L‑PBF) process: an experimental investigation with different non‑destructive techniques N2 - Additive manufacturing (AM) technologies, generally called 3D printing, are widely used because their use provides a high added value in manufacturing complex-shaped components and objects. Defects may occur within the components at different time of manufacturing, and in this regard, non-destructive techniques (NDT) represent a key tool for the quality control of AM components in many industrial fields, such as aerospace, oil and gas, and power industries. In this work, the capability of active thermography and eddy current techniques to detect real imposed defects that are representative of the laser powder bed fusion process has been investigated. A 3D complex shape of defects was revealed by a μCT investigation used as reference results for the other NDT methods. The study was focused on two different types of defects: porosities generated in keyhole mode as well as in lack of fusion mode. Different thermographic and eddy current measurements were carried out on AM samples, providing the capability to detect volumetric irregularly shaped defects using non-destructive methods. KW - Additive Manufacturing KW - Defect detection KW - Thermography KW - Eddy-current testing KW - Micro-computed tomography PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546680 DO - https://doi.org/10.1007/s40964-022-00297-4 SN - 2363-9512 VL - 7 IS - 6 SP - 1239 EP - 1256 PB - Springer AN - OPUS4-54668 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Altenburg, Simon T1 - Process monitoring in metal AM @ BAM - The project ProMoAM N2 - Results of the project ProMoAM (Process monitoring in additive manufacturing) presented. Results from in-situ eddy current testing, optical emission spectroscopy, thermography, optical tomography as well as particle and gas emission spectroscopy are summarized and correlated to results from computed tomography for future in-situ defect detection. T2 - 3rd Meeting of WG6 (NDT in AM) of the EFNDT CY - Online meeting DA - 15.03.2022 KW - Additive Manufacturing KW - Process monitoring KW - ProMoAM PY - 2022 AN - OPUS4-54484 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Altenburg, Simon T1 - Towards hyperspectral in-situ temperature measurement in metal additive manufacturing N2 - The industrial use of additive manufacturing for the production of metallic parts with high geometrical complexity and lot sizes close to one is rapidly increasing as a result of mass individualisation and applied safety relevant constructions. However, due to the high complexity of the production process, it is not yet fully understood and controlled, especially for changing (lot size one) part geometries. Due to the thermal nature of the Laser-powder bed fusion (L-PBF) process – where parts are built up layer-wise by melting metal powder via laser - the properties of the produced part are strongly governed by its thermal history. Thus, a promising route for process monitoring is the use of thermography. However, the reconstruction of temperature information from thermographic data relies on the knowledge of the surface emissivity at each position on the part. Since the emissivity is strongly changing during the process due to phase changes, great temperature gradients, possible oxidation, and other potential influencing factors, the extraction of real temperature data from thermographic images is challenging. While the temperature development in and around the melt pool, where melting and solidification occur is most important for the development of the part properties. Also, the emissivity changes are most severe in this area, rendering the temperature deduction most challenging. A possible route to overcome the entanglement of temperature and emissivity in the thermal radiation is the use of hyperspectral imaging in combination with temperature emissivity separation (TES) algorithms. As a first step towards the combined temperature and emissivity determination in the L-PBF process, here, we use a hyperspectral line camera system operating in the short-wave infrared region (0.9 µm to 1.7 µm) to measure the spectral radiance emitted. In this setup, the melt pool of the L-PBF process migrates through the camera’s 1D field of view, so that the radiation intensities are recorded simultaneously for multiple different wavelength ranges in a spatially resolved manner. At sufficiently high acquisition frame rate, an effective melt pool image can be reconstructed. Using the grey body approximation (emissivity is independent of the wavelength), a first, simple TES is performed, and the resulting emissivity and temperature values are compared to literature values. Subsequent work will include reference measurements of the spectral emissivity in different states allowing its analytical parametrisation as well as the adaption and optimisation of the TES algorithms. An illustration of the proposed method is shown in Fig.1. The investigated method will allow to gain a deeper understanding of the L-PBF process, e.g., by quantitative validation of simulation results. Additionally, the results will provide a data basis for the development of less complex and cheaper sensor technologies for L-PBF in-process monitoring (or for related process), e.g., by using machine learning. T2 - 21st International Conference on Photoacoustic and Photothermal Phenomena CY - Bled, Slovenia DA - 19.06.2022 KW - Thermography KW - Additive manufacturing KW - L-PBF KW - Hyperspectral PY - 2022 AN - OPUS4-55152 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon T1 - Porosity prediction in metal based additive manufacturing utilizing in situ thermography N2 - Quality assessment of components produced by metal based additive manufacturing (AM) technologies such as laser powder bed fusion is rising in importance due to the increased use of AM in industrial production. Here, the presence of internal porosity was identified as a limiting factor for the final component quality. The utilization of thermography as an in-situ monitoring technique allows the determination of the part’s thermal history which was found to be connected to the porosity formation [1]. Combining the local thermal information derived from thermography with the porosity information obtained by x-ray micro computed tomography, machine learning algorithms can be utilized to predict the porosity distribution in the part. In this study, a first approach for the prediction of keyhole porosity in a cylindric specimen from AISI 316L stainless steel is presented. It is based on data augmentation using the “SmoteR” algorithm [2] to cure the dataset imbalance and a 1-dimensional convolutional neural network. [1] C.S. Lough et al., Local prediction of Laser Powder Bed Fusion porosity by short-wave infrared thermal feature porosity probability maps. Journal of Materials Processing Technology, 302, p. 117473 (2022) https://dx.doi.org/10.1016/j.imatprotec.2021.117473 [2] L. Torgo et al., SMOTE for Regression. Progress in Artificial Intelligence, Chapter 33, p. 378-289 (2013) https://dx.doi.org/10.1007/978-3-642-40669-0_33 T2 - KI-Tag Arbeitskreis Chemometrik & Qualitätssicherung - Chemometrics meets Artificial Intelligence CY - Berlin, Germany DA - 01.04.2022 KW - Laser Powder Bed Fusion KW - Thermography KW - Defect Prediction KW - Convolutional Neural Networks PY - 2022 AN - OPUS4-54621 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Roveda, Ilaria A1 - Serrano-Munoz, Itziar A1 - Kromm, Arne A1 - Madia, Mauro T1 - Investigation of residual stresses and microstructure effects on the fatigue behaviour of a L-PBF AlSi10Mg alloy N2 - Al-Si alloys produced by Laser Powder Bed Fusion (L-PBF) techniques allow the fabrication of lightweight free-shape components that find space in aerospace, automotive, biomedical and military applications. Due to the high cooling rates occurring during the building process, L-PBF AlSi10Mg alloys exhibit an ultra-fine microstructure that leads to superior mechanical properties in the as-built condition compared to conventional cast Al-Si materials. Nevertheless, L-PBF processing induces high thermal gradients, leading to deleterious residual stress levels that must be considered to avoid part distortion and unpredicted failures. In order to relax detrimental residual stress and to increase the ductility, post-processing stress relief treatments are generally performed. In as-built condition the hypoeutectic AlSi10Mg microstructure consist of fine α-Al cells containing uniformly dispersed silicon nanoparticles, which are, in addition, surrounded by a eutectic Si network. Above 260°C the silicon interconnectivity starts to breakdown into spheroidized particles and to coarsen. At the same time, the heating residual stresses are relieved. The objective of the contribution is to investigate, under different heat treatment conditions, the evolution of microstructure and residual stresses in view of optimizing the fatigue performance of the alloy. To this purpose various heat treatments in a range of temperatures between 265°C and 300°C for a duration between 15 minutes and 2 hours are performed. The microstructure modifications are analysed using a scanning electron microscope and the residual stress state is measured by laboratory X-ray diffraction. T2 - FATIGUE DESIGN 2021 CY - Online meeting DA - 17.11.2021 KW - AlSi10Mg alloy KW - Additive manufacturing KW - L-PBF KW - Residual stress KW - Heat treatment PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-544942 DO - https://doi.org/10.1016/j.prostr.2022.03.057 SN - 2452-3216 VL - 38 SP - 564 EP - 571 PB - Elsevier B.V. AN - OPUS4-54494 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Mishurova, Tatiana A1 - Serrano-Munoz, Itziar T1 - Multiscale residual stress analysis and synchrotron X-ray refraction of additively manufactured parts N2 - The overview of the activity of group 8.5 Micro-NDT (BAM, Belin, Germany) in the field of additively manufacturing material characterization will be presented. The challenges in the residual stress analysis of AM components are discussed on the basis on the show studies performed in BAM. Also, the synchrotron X-ray refraction technique, available in BAM, is presented, showing example of in-situ heating test of Al10SiMg AM material. T2 - Seminar at Grenoble INP, Science et Ingénierie des Matériaux et Procédés (SIMaP) CY - Grenoble, France DA - 01.07.2022 KW - Additive manufacturing KW - Residual stress KW - X-ray refraction KW - Computed tomography PY - 2022 AN - OPUS4-55232 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Roveda, Ilaria T1 - Effect of heat treatment on the microstructure, residual stress state and fatigue properties of PBF-LB/M AlSi10Mg N2 - Al-Si alloys produced by Laser Powder Bed Fusion (PBF-LB/M) techniques allow the fabrication of lightweight free-shape components. Due to the extremely heterogeneous cooling and heating, PBF-LB/M induces high magnitude residual stress (RS) and a fine Si microstructure. As the RS can be deleterious to the fatigue resistance of engineering components, great efforts are focused on understanding their evolution before and after post-process heat treatments (HT). T2 - Alloys for Additive Manufacturing Symposium 2022 (AAMS22) CY - Munich, Germany DA - 12.09.2022 KW - Neutron diffraction KW - X-ray diffraction KW - Crack propagation PY - 2022 AN - OPUS4-55871 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Tang, Lei A1 - Magdysyuk, Oxana V. A1 - Jiang, Fuqing A1 - Wang, Yiqiang A1 - Evans, Alexander A1 - Kabra, Saurabh A1 - Cai, Biao T1 - Mechanical performance and deformation mechanisms at cryogenic temperatures of 316L stainless steel processed by laser powder bed fusion: In situ neutron diffraction N2 - Manufacturing austenitic stainless steels (ASSs) using additive manufacturing is of great interest for cryogenic applications. Here, the mechanical and microstructural responses of a 316L ASS built by laser powder bed fusion were revealed by performing in situ neutron diffraction tensile tests at the low-temperature range (from 373 to 10 K). The stacking fault energy almost linearly decreased from 29.2 ± 3.1 mJm⁻² at 373 K to 7.5 ± 1.7 mJm⁻² at 10 K, with a slope of 0.06 mJm⁻²K⁻¹, leading to the transition of the dominant deformation mechanism from strain-induced twinning to martensite formation. As a result, excellent combinations of strength and ductility were achieved at the low-temperature range. KW - Condensed Matter Physics KW - General Materials Science KW - Mechanics of Materials KW - Metals and Alloys KW - Mechanical Engineering PY - 2022 DO - https://doi.org/10.1016/j.scriptamat.2022.114806 SN - 1359-6462 VL - 218 SP - 1 EP - 7 PB - Elsevier BV CY - Amsterdam AN - OPUS4-59317 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Becker, Tina A1 - Altenburg, Simon A1 - Scheuschner, Nils A1 - Breese, Philipp Peter A1 - Metz, C. A1 - Hilgenberg, Kai A1 - Maierhofer, Christiane T1 - In-situ monitoring of the Laser Powder Bed Fusion build process via bi- chromatic optical tomography N2 - As metal additive manufacturing (AM) is entering industrial serial production of safety relevant components, the need for reliable process qualification is growing continuously. Especially in strictly regulated industries, such as aviation, the use of AM is strongly dependent on ensuring consistent quality of components. Because of its numerous influencing factors, up to now, the metal AM process is not fully controllable. Today, expensive part qualification processes for each single component are common in industry. This contribution focusses on bi-chromatic optical tomography as a new approach for AM in-situ quality control. In contrast to classical optical tomography, the emitted process radiation is monitored simultaneously with two temperature calibrated cameras at two separate wavelength bands. This approach allows one to estimate the local maximum temperatures during the manufacturing process, thus increases the comparability of monitoring data of different processes. A new process information level at low investment cost is reachable, compared to, e.g., infrared thermography. T2 - LANE 2022 CY - Fürth, Germany DA - 04.08.2022 KW - Optical tomography KW - Additive Manufacturing KW - L-PBF KW - In-process monitoring PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-560270 DO - https://doi.org/10.1016/j.procir.2022.08.035 SN - 2212-8271 VL - 111 SP - 340 EP - 344 PB - Elsevier B.V. CY - Amsterdam AN - OPUS4-56027 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Becker, Tina T1 - Two approaches for multi measurand in-situ monitoring of the L-PBF process – bicolor- and RGB-optical tomography N2 - Since metal additive manufacturing (AM) becomes more and more established in industry, also the cost pressure for AM components increases. One big cost factor is the quality control of the manufactured components. Reliable in-process monitoring systems are a promising route to lower scrap rates and enhance trust in the component and process quality. The focus of this contribution is the presentation and comparison of two optical tomography based multi measurand in-situ monitoring approaches for the L-PBF process: the bicolor- and the RGB-optical tomography. The classical optical tomography (OT) is one of the most common commercial in-situ monitoring techniques in industrial L-PBF machines. In the OT spatial resolved layer-images of the L-PBF process are taken from an off-axis position in one near infrared wavelength window. In addition to the explanatory powers classical OT, both here presented approaches enable the determination of the maximum surface temperature. In contrast to thermography that may also yield maximum temperature information, the needed equipment is significantly cheaper and offers a higher spatial resolution. Both approaches are implemented at a new in-house developed L-PBF system (Sensor-based additive manufacturing machine - SAMMIE). SAMMIE is specifically designed for the development and characterization of in-situ monitoring systems and is introduced as well. T2 - ICAM2022 CY - Orlando, FL, USA DA - 31.10.2022 KW - Additive Manufacturing KW - L-PBF KW - In-process monitoring KW - Optical tomography PY - 2022 AN - OPUS4-56594 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon T1 - Machine Learning based defect detection in Laser Powder Bed Fusion utilizing thermographic feature data N2 - The formation of irregularities such as keyhole porosity pose a major challenge to the manufacturing of metal parts by laser powder bed fusion (PBF-LB/M). In-situ thermography as a process monitoring technique shows promising potential in this manner since it is able to extract the thermal history of the part which is closely related to the formation of irregularities. In this study, we investigate the utilization of machine learning algorithms to detect keyhole porosity on the base of thermographic features. Here, as a referential technique, x-ray micro computed tomography is utilized to determine the part's porosity. An enhanced preprocessing workflow inspired by the physics of the keyhole irregularity formation is presented in combination with a customized model architecture. Furthermore, experiments were performed to clarify the role of important parameters of the preprocessing workflow for the task of defect detection . Based on the results, future demands on irregularity prediction in PBF-LB/M are derived. T2 - International Conference on NDE 4.0 CY - Berlin, Germany DA - 24.10.2022 KW - Laser Powder Bed Fusion KW - Thermography KW - In-situ Monitoring KW - Convolutional Neural Networks PY - 2022 AN - OPUS4-56332 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon T1 - Defect prediction on the Base of Thermographic features in Laser Powder Bed Fusion Utilizing Machine Learning Algorithms N2 - Avoiding the formation of defects such as keyhole pores is a major challenge for the production of metal parts by Laser Powder Bed Fusion (LPBF). The use of in-situ monitoring by thermographic cameras is a promising approach to detect defects, however the data is hard to analyze by conventional algorithms. Therefore, we investigate the use of Machine Learning (ML) in this study, as it is a suitable tool to model complex processes with many influencing factors. A ML model for defect prediction is created based on features extracted from process thermograms. The porosity information calculated from an x-ray Micro Computed Tomography (µCT) scan is used as reference. Physical characteristics of the keyhole pore formation are incorporated into the model to increase the prediction accuracy. Based on the prediction result, the quality of the input data is inferred and future demands on in-situ monitoring of LPBF processes are derived. T2 - Additive Manufacturing Benchmarks 2022 CY - Bethesda, MA, USA DA - 14.08.2022 KW - Laser Powder Bed Fusion KW - Thermography KW - In-situ Monitoring KW - Machine Learning KW - Defect prediction PY - 2022 AN - OPUS4-55591 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon T1 - Defect prediction in laser powder bed fusion based on thermographic features utilizing convolutional neural networks N2 - The appearance of irregularities such as keyhole porosity is a major challenge for the production of metal parts by laser powder bed fusion (PBF-LB/M). The utilization of thermographic in-situ monitoring is a promising approach to extract the thermal history which is closely related to the formation of irregularities. In this study, we investigate the utilization of convolutional neural networks to predict keyhole porosity based on thermographic features. Here, the porosity information calculated from an x-ray micro computed tomography scan is used as reference. Feature engineering is performed to enable the model to learn the complex physical characteristics of the porosity formation. The model is examined with regard to the choice of hyperparameters, the significance of thermal features and characteristics of the data acquisition. Based on the results, future demands on irregularity prediction in PBF-LB/M are derived. T2 - GIMC SIMAI YOUNG 2022 CY - Pavia, Italy DA - 29.09.2022 KW - Laser Powder Bed Fusion KW - Thermography KW - In-situ Monitoring KW - Convolutional Neural Networks PY - 2022 AN - OPUS4-56331 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Przyklenk, A. T1 - Progress of the European Metrology Network for Advanced Manufacturing N2 - The European Metrology Network (EMN) for Advanced Manufacturing has been established in June 2021. Currently nine EMNs focussing on different important topics of strategic importance for Europe exist and form an integral part of EURAMET, the European Association of National Metrology Institutes (NMI). EMNs are tasked to ▪ develop a high-level coordination of the metrology community in Europe in a close dialogue with the respective stakeholders (SH) ▪ develop a strategic research agenda (SRA) within their thematic areas ▪ provide contributions to the European Partnership on Metrology research programme Based on the analysis of existing metrology infrastructures and capabilities of NMIs, the metrology research needs for advanced manufacturing are identified in close cooperation with academic, governmental and industrial stakeholders. Here, we report on the progress of the EMN for Advanced Manufacturing. T2 - Euspen 22nd International Conference & Exhibition (Euspen 2022) CY - Genf, Switzerland DA - 30.05.2022 KW - JNP AdvManuNet KW - Metrology KW - Advanced manufacturing KW - European Metrology Network KW - EMN PY - 2022 AN - OPUS4-55805 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Mishurova, Tatiana T1 - In-situ imaging of additively manufactured alloys at the BAMline N2 - In this work, we present the recent in-situ imaging developments at the BAMline (of synchrotron BESSY II, HZB), focused on the in-situ characterization and understanding of microstructural evolution of additively manufactured materials subjected to different environments. Two show cases are presented. In the first, X-ray refraction radiography (SXRR) was combined with in-situ heat treatment to monitor the microstructural evolution as a function of temperature in a laser powder bed fusion (LPBF) manufactured AlSi10Mg alloy. We show that SXRR allows detecting the changes in the Si-phase morphology upon heating using statistically relevant volumes. SXRR also allows observing the growth of pores (i.e., thermally induced porosity), usually studied via X-ray computed tomography (XCT), but using much smaller fields-of-view. In the second case study, XCT was combined with in-situ tensile test to investigate the damage mechanism in a LPBF Aluminum Metal Matrix Composite (MMC). In-situ SXCT test disclosed the critical role of the defects in the failure mechanism along with pre-cracks in the reinforcement phase of MMC. We found that cracks were initiated from lack-of-fusion defects and propagated through coalescence with other defects. T2 - New Frontiers in Materials Design for Laser Additive Manufacturing CY - Montabaur, Germany DA - 22.05.22 KW - Additive manufacturing KW - Laser powder bed fusion KW - Synchrotron X-ray computed tomography KW - Synchrotron X-ray Refraction PY - 2022 AN - OPUS4-54900 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -