TY - JOUR A1 - Mohr, Gunther A1 - Chaudry, Mohsin Ali A1 - Scheuschner, Nils A1 - Blasón Gonzalez, Sergio A1 - Madia, Mauro A1 - Hilgenberg, Kai T1 - Thermal history transfer from complex components to representative test specimens in laser powder bed fusion N2 - Additively manufactured components are characterized by heterogeneous mechanical properties due to variations of the microstructure, flaws and residual stresses resulting from the inhomogeneous fabrication process. The large number of influencing factors poses a further challenge in understanding the correlation between material properties, process parameters and component geometry. Therefore, the qualification of components based on witness specimens produced within the same job is questionable. This work aims to present a new strategy for the characterization of PBF-LB/M components based on representative specimens. The key assumption is the feasibility of a transfer of the thermal history from a component to a specimen. It is assumed that similar material properties are determined for components and specimens produced adopting a similar thermal history. After the definition of a region of interest in the component, a combination of thermal analyses by means of finite elements and in-situ experimental determination of the thermal history through infrared thermography is used to produce test coupons with a similar thermal history. The effectiveness of the procedure is demonstrated on a pressure vessel for applications in the chemical industry. KW - Additive manufacturing KW - Heat accumulation KW - Thermal history KW - In situ monitoring KW - Process simulation KW - Representative specimens PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-602651 DO - https://doi.org/10.1007/s40964-024-00689-8 SN - 2363-9512 SN - 2363-9520 SP - 1 EP - 16 PB - Springer CY - Cham, Switzerland AN - OPUS4-60265 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mohr, Gunther A1 - Nowakowski, Susanna A1 - Altenburg, Simon A1 - Maierhofer, Christiane A1 - Hilgenberg, Kai T1 - Experimental Determination of the Emissivity of Powder Layers and Bulk Material in Laser Powder Bed Fusion Using Infrared Thermography and Thermocouples N2 - Recording the temperature distribution of the layer under construction during laser powder bed fusion (L-PBF) is of utmost interest for a deep process understanding as well as for quality assurance and in situ monitoring means. While having a notable number of thermal monitoring approaches in additive manufacturing (AM), attempts at temperature calibration and emissivity determination are relatively rare. This study aims for the experimental temperature adjustment of an off-axis infrared (IR) thermography setup used for in situ thermal data acquisition in L-PBF processes. The temperature adjustment was conducted by means of the so-called contact method using thermocouples at two different surface conditions and two different materials: AISI 316L L-PBF bulk surface, AISI 316L powder surface, and IN718 powder surface. The apparent emissivity values for the particular setup were determined. For the first time, also corrected, closer to real emissivity values of the bulk or powder surface condition are published. In the temperature region from approximately 150 °C to 580 °C, the corrected emissivity was determined in a range from 0.2 to 0.25 for a 316L L-PBF bulk surface, in a range from 0.37 to 0.45 for 316L powder layer, and in a range from 0.37 to 0.4 for IN718 powder layer. KW - Emisssivity KW - Laser Powder Bed Fusion (L-PBF) KW - Selective Laser Melting (SLM) KW - Additive Manufacturing (AM) KW - Process monitoring KW - Infrared thermography PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-516148 DO - https://doi.org/10.3390/met10111546 VL - 10 IS - 11 SP - 1546 PB - MDPI CY - Basel, Schweiz AN - OPUS4-51614 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 - JOUR A1 - Mohr, Gunther A1 - Sommer, Konstantin A1 - Knobloch, Tim A1 - Altenburg, Simon A1 - Recknagel, Sebastian A1 - Bettge, Dirk A1 - Hilgenberg, Kai T1 - Process Induced Preheating in Laser Powder Bed Fusion Monitored by Thermography and Its Influence on the Microstructure of 316L Stainless Steel Parts N2 - Undetected and undesired microstructural variations in components produced by laser powder bed fusion are a major challenge, especially for safety-critical components. In this study, an in-depth analysis of the microstructural features of 316L specimens produced by laser powder bed fusion at different levels of volumetric energy density and different levels of inter layer time is reported. The study has been conducted on specimens with an application relevant build height (>100 mm). Furthermore, the evolution of the intrinsic preheating temperature during the build-up of specimens was monitored using a thermographic in-situ monitoring set-up. By applying recently determined emissivity values of 316L powder layers, real temperatures could be quantified. Heat accumulation led to preheating temperatures of up to about 600 °C. Significant differences in the preheating temperatures were discussed with respect to the individual process parameter combinations, including the build height. A strong effect of the inter layer time on the heat accumulation was observed. A shorter inter layer time resulted in an increase of the preheating temperature by more than a factor of 2 in the upper part of the specimens compared to longer inter layer times. This, in turn, resulted in heterogeneity of the microstructure and differences in material properties within individual specimens. The resulting differences in the microstructure were analyzed using electron back scatter diffraction and scanning electron microscopy. Results from chemical analysis as well as electron back scatter diffraction measurements indicated stable conditions in terms of chemical alloy composition and austenite phase content for the used set of parameter combinations. However, an increase of the average grain size by more than a factor of 2.5 could be revealed within individual specimens. Additionally, differences in feature size of the solidification cellular substructure were examined and a trend of increasing cell sizes was observed. This trend was attributed to differences in solidification rate and thermal gradients induced by differences in scanning velocity and preheating temperature. A change of the thermal history due to intrinsic preheating could be identified as the main cause of this heterogeneity. It was induced by critical combinations of the energy input and differences in heat transfer conditions by variations of the inter layer time. The microstructural variations were directly correlated to differences in hardness. KW - Additive manufacturing KW - Laser powder bed fusion KW - In-situ process monitoring KW - Thermography KW - Heat accumulation KW - Inter layer time KW - Cellular substructure PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-529240 DO - https://doi.org/10.3390/met11071063 VL - 11 IS - 7 SP - 1063 PB - MDPI CY - Basel, Schweiz AN - OPUS4-52924 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Nadammal, Naresh A1 - Mishurova, Tatiana A1 - Fritsch, Tobias A1 - Serrano-Munoz, Itziar A1 - Kromm, Arne A1 - Haberland, C. A1 - Portella, Pedro Dolabella A1 - Bruno, Giovanni T1 - Critical role of scan strategies on the development of microstructure, texture, and residual stresses during laser powder bed fusion additive manufacturing N2 - Laser based powder bed fusion additive manufacturing offers the flexibility to incorporate standard and userdefined scan strategies in a layer or in between the layers for the customized fabrication of metallic components. In the present study, four different scan strategies and their impact on the development of microstructure, texture, and residual stresses in laser powder bed fusion additive manufacturing of a nickel-based superalloy Inconel 718 was investigated. Light microscopy, scanning electron microscopy combined with electron backscatter diffraction, and neutron diffraction were used as the characterization tools. Strong textures with epitaxially grown columnar grains were observed along the build direction for the two individual scan strategies. Patterns depicting the respective scan strategies were visible in the build plane, which dictated the microstructure development in the other planes. An alternating strategy combining the individual strategies in the successive layers and a 67◦ rotational strategy weakened the texture by forming finer microstructural features. Von Mises equivalent stress plots revealed lower stress values and gradients, which translates as lower distortions for the alternating and rotational strategies. Overall results confirmed the scope for manipulating the microstructure, texture, and residual stresses during laser powder bed fusion additive manufacturing by effectively controlling the scan strategies. KW - Additive manufacturing KW - Laser powder bed fusion KW - Nickel-based superalloys KW - Scan strategies KW - Residual stresses KW - Microstructure and texture PY - 2021 DO - https://doi.org/10.1016/j.addma.2020.101792 VL - 38 SP - 1792 PB - Elsevier B.V. AN - OPUS4-51944 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - O'Connor, Daniel T1 - European Metrology Network (EMN) for Advanced Manufacturing Development of the Strategic Research Agenda (SRA) N2 - The European Commission has identified Advanced Manufacturing and Advanced Materials as two of six Key Enabling Technologies (KETs). It is considered that Metrology is a key enabler for the advancement of these KETs. Consequently, EURAMET, the association of metrology institutes in Europe, has strengthened the role of Metrology for these KETs by enabling the creation of a European Metrology Network (EMN) for Advanced Manufacturing. The EMN is comprised of National Metrology Institutes (NMIs) and Designated Institutes (DIs) from across Europe and was formally established in October 2021. The aim of the EMN is to provide a high-level coordination of European metrology activities for the Advanced Manufacturing community. The EMN itself is organized in three sections representing the major stages of the manufacturing chain: 1) Advanced Materials, 2) Smart Manufacturing Systems, and 3) Manufactured Components & Products. The EMN for Advanced Manufacturing is engaging with stakeholders in the field of Advanced Manufacturing (large companies & SMEs, industry organisations, existing networks, and academia), as well as the wider Metrology community, including Technical Committees, to provide input for the Strategic Research Agenda (SRA) on Metrology for Advanced Manufacturing. This contribution will give an overview about the first version of the SRA prepared by the EMN for Advanced Manufacturing. T2 - Euspen, 23rd International Conference & Exhibitio CY - Copenhagen, Danmark DA - 13.06.2023 KW - European Metrology Network (EMN) KW - Advanced Manufacturing KW - Metrology KW - Strategic Research Agenda (SRA) PY - 2023 AN - OPUS4-59176 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - O'Connor, Daniel A1 - Evans, Alexander A1 - Balsamo, Alessandro A1 - Favres, Georges A1 - Przyklenk, Anita A1 - Bosse, Harald A1 - Phillips, Dishi T1 - European Metrology Network (EMN) for Advanced Manufacturing ─ Development of the Strategic Research Agenda (SRA) N2 - The European Commission has identified Advanced Manufacturing and Advanced Materials as two of six Key Enabling Technologies (KETs). It is considered that Metrology is a key enabler for the advancement of these KETs. Consequently, EURAMET, the association of metrology institutes in Europe, has strengthened the role of Metrology for these KETs by enabling the creation of a European Metrology Network (EMN) for Advanced Manufacturing. The EMN is comprised of National Metrology Institutes (NMIs) and Designated Institutes (DIs) from across Europe and was formally established in October 2021. The aim of the EMN is to provide a high-level coordination of European metrology activities for the Advanced Manufacturing community. The EMN itself is organized in three sections representing the major stages of the manufacturing chain: 1) Advanced Materials, 2) Smart Manufacturing Systems, and 3) Manufactured Components & Products. The EMN for Advanced Manufacturing is engaging with stakeholders in the field of Advanced Manufacturing (large companies & SMEs, industry organisations, existing networks, and academia), as well as the wider Metrology community, including Technical Committees, to provide input for the Strategic Research Agenda (SRA) on Metrology for Advanced Manufacturing. This contribution will give an overview about the first version of the SRA prepared by the EMN for Advanced Manufacturing T2 - Euspen, 23rd International Conference & Exhibitio CY - Copenhagen, Danmark DA - 12.06.2023 KW - European Metrology Network (EMN) KW - Advanced Manufacturing KW - Metrology KW - Strategic Research Agenda (SRA) PY - 2023 SP - 363 EP - 364 AN - OPUS4-59196 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Obaton, A.-F. A1 - Fain, J. A1 - Meinel, Dietmar A1 - Tsamos, Athanasios A1 - Léonard, F. A1 - Lécuelle, B. A1 - Djemaï, M. T1 - In Vivo Bone Progression in and around Lattice Implants Additively Manufactured with a New Titanium Alloy N2 - The osseointegration in/around additively manufactured (AM) lattice structures of a new titanium alloy, Ti–19Nb–14Zr, was evaluated. Different lattices with increasingly high sidewalls gradually closing them were manufactured and implanted in sheep. After removal, the bone–interface implant (BII) and bone–implant contact (BIC) were studied from 3D X-ray computed tomography images. Measured BII of less than 10 µm and BIC of 95% are evidence of excellent osseointegration. Since AMnaturally leads to a high-roughness surface finish, the wettability of the implant is increased. The new alloy possesses an increased affinity to the bone. The lattice provides crevices in which the biological tissue can jump in and cling. The combination of these factors is pushing ossification beyond its natural limits. Therefore, the quality and speed of the ossification and osseointegration in/around these Ti–19Nb–14Zr laterally closed lattice implants open the possibility of bone spline key of prostheses. This enables the stabilization of the implant into the bone while keeping the possibility of punctual hooks allowing the implant to be removed more easily if required. Thus, this new titanium alloy and such laterally closed lattice structures are appropriate candidates to be implemented in a new generation of implants. KW - Osseointegration KW - X-ray computed tomography KW - Additive manufacturing KW - Machine learning segmentation PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-577066 DO - https://doi.org/10.3390/app13127282 VL - 13 IS - 12 SP - 1 EP - 18 PB - MDPI CY - Basel, Switzerland AN - OPUS4-57706 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon T1 - Multispectral in-situ monitoring of a L-PBF manufacturing process using three thermographic camera systems N2 - The manufacturing of metal parts for the use in safety-relevant applications by Laser Powder Bed Fusion (L-PBF) demands a quality assurance of both part and process. Thermography is a nondestructive testing method that allows the in-situ determination of the thermal history of the produced part which is connected to the mechanical properties and the formation of defects [1]. A wide range of commercial thermographic camera systems working in different spectral ranges is available on the market. The understanding of the applicability of these cameras for qualitative and quantitative in-situ measurements in L-PBF is of vital importance [2]. In this study, the building process of a cylindrical specimen (Inconel 718) is monitored by three camera systems simultaniously. These camera systems are sensitive in various spectral bandwidths providing information in different temperature ranges. The performance of each camera system is explored in the context of the extraction of image features for the detection of defects. It is shown that the high temporal and thermal process dynamics are limiting factors on this matter. The combination of different spectral camera systems promises the potential of an improved defect detection by data fusion. T2 - LASER SYMPOSIUM & ISAM 2021 CY - Online meeting DA - 07.12.2021 KW - Laser Powder Bed Fusion KW - Thermography KW - In-situ Monitoring KW - Defect detection PY - 2021 AN - OPUS4-54141 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 - CONF A1 - Oster, 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 T1 - Potentials and challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring in PBF-LB/M 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 im Breisgau, 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 AN - OPUS4-59192 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 - A comparison of machine learning approaches for porosity prediction in PBF-LB/M based on thermography N2 - Metal-based additive manufacturing processes are increasingly used in industry to produce complex-shaped components. In this regard, the laser-based Powder Bed Fusion process (PBF-LB/M) is one of the key technologies due to its capability to produce components in high spatial accuracy. The formation of porosity during manufacturing poses a serious risk to the safety of the printed parts. For quality assessment, in-situ monitoring technologies such as thermography can be used to capture the thermal history during production. It was shown that discontinuities within the thermal history can be correlated with the probability of porosity or defect formation. In this context, Machine Learning (ML) algorithms have achieved promising results for the task of porosity prediction based on thermographic in-situ monitoring data. One important technique is the use of thermogram features for porosity prediction that are extracted from the raw data (e.g., features related to the melt pool geometry or spatter generation). However, the reduction from large thermogram data to discrete features holds the risk of losing potentially important thermal information and, thereby, introducing bias in the model. Therefore, we present a raw data-based deep learning approach that uses thermographic image sequences for the prediction of local porosity. The model takes advantage of the self-attention mechanism that considers not only the thermogram information but also its positional context within the sequence. The model is used to predict porosity in the form of a many-to-one regression. It is trained and tested on a dataset retrieved from the manufacturing of HAYNES282 cuboid specimens. The model results are compared against state-of-the-art thermogram feature-based ML models and artificial neural networks. The raw data model outperforms its feature-based counterparts in terms of prediction scores and, therefore, seems to make better use of the information available in the thermogram data. T2 - 4th Symposium on Materials and Additive Manufacturing CY - Berlin, Germany DA - 12.06.2024 KW - PBF-LB/M KW - In situ monitoring KW - Thermography KW - Additive Manufacturing KW - Machine learning KW - Porosity prediction PY - 2024 AN - OPUS4-62472 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 - 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 - 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 - 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 - Oster, Simon A1 - Fritsch, Tobias A1 - Ulbricht, Alexander A1 - Mohr, Gunther A1 - Bruno, Giovanni A1 - Maierhofer, Christiane A1 - Altenburg, Simon T1 - On the Registration of Thermographic In Situ Monitoring Data and Computed Tomography Reference Data in the Scope of Defect Prediction in Laser Powder Bed Fusion N2 - The detection of internal irregularities is crucial for quality assessment in metal-based additive manufacturing (AM) technologies such as laser powder bed fusion (L-PBF). The utilization of in-process thermography as an in situ monitoring tool in combination with post-process X-ray micro computed tomography (XCT) as a reference technique has shown great potential for this aim. Due to the small irregularity dimensions, a precise registration of the datasets is necessary as a requirement for correlation. In this study, the registration of thermography and XCT reference datasets of a cylindric specimen containing keyhole pores is carried out for the development of a porosity prediction model. The considered datasets show variations in shape, data type and dimensionality, especially due to shrinkage and material elevation effects present in the manufactured part. Since the resulting deformations are challenging for registration, a novel preprocessing methodology is introduced that involves an adaptive volume adjustment algorithm which is based on the porosity distribution in the specimen. Thus, the implementation of a simple three-dimensional image-to-image registration is enabled. The results demonstrate the influence of the part deformation on the resulting porosity location and the importance of registration in terms of irregularity prediction. KW - Selective laser melting (SLM) KW - Laser powder bed fusion (L-PBF) KW - Additive manufacturing (AM) KW - Process monitoring KW - Infrared thermography KW - X-ray computed tomography (XCT) KW - Defect detection KW - Image registration PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-549412 DO - https://doi.org/10.3390/met12060947 VL - 12 IS - 6 SP - 1 EP - 21 PB - MDPI AN - OPUS4-54941 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 - JOUR A1 - Oster, Simon A1 - Scheuschner, Nils A1 - Chand, Keerthana A1 - Altenburg, Simon T1 - Local porosity prediction in metal powder bed fusion using in-situ thermography: A comparative study of machine learning techniques N2 - The formation of flaws such as internal porosity in parts produced by Metal-based Powder Bed Fusion with Laser Beam (PBF-LB/M) significantly hinders its broader industrial application, as porosity can potentially lead to part failure. Addressing this issue, this study explores the efficacy of in-situ thermography, particularly short-wave infrared thermography, for detecting and predicting porosity during manufacturing. This technique is capable of monitoring the part’s thermal history which is closely connected to the flaw formation process. Recent advancements in Machine Learning (ML) have been increasingly leveraged for porosity prediction in PBF-LB/M. However, previous research primarily focused on global rather than localized porosity prediction which simplified the complex prediction task. Thereby, the opportunity to correlate the predicted flaw position with expected part strain to judge the severity of the flaw for part performance is neglected. This study aims to bridge this gap by studying the potential of SWIR thermography for predicting local porosity levels using regression models. The models are trained on data from two identical HAYNES®282® specimens. We compare the effectiveness of feature-based and raw data-based models in predicting different porosity types and examine the importance of input data in porosity prediction. We show that models trained on SWIR thermogram data can identify systematic trends in local flaw formation. This is demonstrated for forced flaw formation using process parameter shifts and, moreover, for randomly formed flaws in the specimen bulk. Furthermore, we identify features of high importance for the prediction of lack-of-fusion and keyhole porosity from SWIR monitoring data. KW - PBF-LB/M KW - In situ monitoring KW - Thermography KW - Additive Manufacturing KW - Process monitoring KW - Porosity prediction KW - Machine Learning KW - Feature extraction PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-621798 DO - https://doi.org/10.1016/j.addma.2024.104502 SN - 2214-7810 VL - 95 SP - 1 EP - 17 PB - Elsevier B.V. AN - OPUS4-62179 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 - 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 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 - JOUR A1 - Pacheco, V. A1 - Marattukalam, J. J. A1 - Karlsson, D. A1 - Dessieux, L. A1 - Tran, K. V. A1 - Beran, P. A1 - Manke, I. A1 - Kardjilov, N. A1 - Markötter, Henning A1 - Sahlberg, M. A1 - Woracek, R. T1 - On the relationship between laser scan strategy, texture variations and hidden nucleation sites for failure in laser powder-bed fusion N2 - While laser powder-bed fusion has overcome some of the design constraints of conventional manufacturing methods, it requires careful selection of process parameters and scan strategies to obtain favorable properties. Here we show that even simple scan strategies, complex ones being inevitable when printing intricate designs, can inadvertently produce local alterations of the microstructure and preferential grain orientation over small areas – which easily remain unnoticed across the macroscale. We describe how a combined usage of neutron imaging and electron backscatter diffraction can reveal these localized variations and explain their origin within cm-sized parts. We explain the observed contrast variations by linking the neutron images to simulated data, pole figures and EBSD, providing an invaluable reference for future studies and showing that presumably minor changes of the scan strategy can have detrimental effects on the mechanical properties. In-situ tensile tests reveal that fracture occurs in a region that was re-melted during the building process. KW - Laser powder-bed fusion KW - Texture KW - Preferential orientation KW - Diffraction contrast neutron imaging KW - Bragg-edge PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-568054 DO - https://doi.org/10.1016/j.mtla.2022.101614 VL - 26 SP - 1 EP - 9 PB - Elsevier B.V. AN - OPUS4-56805 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Pauzon, C. A1 - Mishurova, Tatiana A1 - Evsevleev, Sergei A1 - Dubiez-Le Goff, S. A1 - Murugesan, S. A1 - Bruno, Giovanni A1 - Hryha, E. T1 - Residual stresses and porosity in Ti-6Al-4V produced by laser powder bed fusion as a function of process atmosphere and component design N2 - The influence of the process gas, laser scan speed, and sample thickness on the build-up of residual stresses and porosity in Ti-6Al-4V produced by laser powder bed fusion was studied. Pure argon and helium, as well as a mixture of those (30% helium), were employed to establish process atmospheres with a low residual Oxygen content of 100 ppm O2. The results highlight that the subsurface residual stresses measured by X-ray diffraction were significantly lower in the thin samples (220 MPa) than in the cuboid samples (645 MPa). This difference was attributed to the shorter laser vector length, resulting in heat accumulation and thus in-situ stress relief. The addition of helium to the process gas did not introduce additional subsurface residual stresses in the simple geometries, even for the increased scanning speed. Finally, larger deflection was found in the cantilever built under helium (after removal from the baseplate), than in those produced under argon and an argon-helium mixture. This result demonstrates that complex designs involving large scanned areas could be subjected to higher residual stress when manufactured under helium due to the gas’s high thermal conductivity, heat capacity, and thermal diffusivity. KW - Additive manufacturing KW - Ti-6Al-4V KW - Residual stress KW - Process atmosphere KW - Helium PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-534209 DO - https://doi.org/10.1016/j.addma.2021.102340 VL - 47 SP - 2340 PB - Elsevier B.V. AN - OPUS4-53420 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Pauzon, C. A1 - Mishurova, Tatiana A1 - Fischer, M. A1 - Ahlström, J. A1 - Fritsch, Tobias A1 - Bruno, Giovanni A1 - Hryha, Eduard T1 - Impact of contour scanning and helium-rich process gas on performances of Alloy 718 lattices produced by laser powder bed fusion N2 - Contour scanning and process gas type are process parameters typically considered achieving second order effects compared to first order factors such as laser power and scanning speed. The present work highlights that contour scanning is crucial to ensure geometrical accuracy and thereby the high performance under uniaxial compression of complex Alloy 718 lattice structures. Studies of X-ray computed tomography visualizations of as-built and compression-strained structures reveal the continuous and smooth bending and compression of the walls, and the earlier onset of internal contact appearance in the denser lattices printed with contour. In contrast, the effect of addition of He to the Ar process gas appears to have limited influence on the mechanical response of the lattices and their microstructure as characterized by electron backscattered diffraction. However, the addition of He proved to significantly enhance the cooling rate and to reduce the amount of the generated spatters as evidenced by in situ monitoring of the process emissions, which is very promising for the process stability and powder reusability during laser powder bed fusion. KW - Additive manufacturing KW - Laser powder bed fusion KW - Gyroid lattice KW - Process atmosphere PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546632 DO - https://doi.org/10.1016/j.matdes.2022.110501 SN - 0264-1275 VL - 215 SP - 110501 PB - Elsevier Ltd. AN - OPUS4-54663 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pittner, Andreas T1 - Wire arc additive manufacturing of high strength AL-MG-SI alloys N2 - Gas metal arc-based direct energy deposition (DED) additive manufacturing technologies hold significant promise for producing large-scale metal components. However, determining optimal process parameters to achieve the desired near-net shape design and mechanical performance is a complex challenge due to the intricate interplay between process parameters and material properties. This presentation highlights the use of a newly developed solid welding wire doped with TiB to improve grain refinement in metals deposited via DED-Arc for high-strength precipitation-hardening AlMgSi aluminum alloys. This wire stems from our initial metallurgical investigations into grain refinement in aluminum weld metals. The research emphasizes the relationship between process parameters and component quality, aiming to uncover the mechanisms that influence performance. This is essential for establishing a reliable range of parameters that meet quality standards, many of which are derived from traditional welding practices. Component quality was assessed by studying pore size, pore distribution, and grain morphology. To further enhance the mechanical properties of the deposited material, post-weld heat treatments—including solution treatment, quenching, and artificial aging—were applied. The study also explores how different heat treatment strategies affect the final mechanical properties. To validate the feasibility of 3D printing high-strength aluminum alloys, a more complex demonstrator component was fabricated. The results demonstrate that DED-Arc technology can produce large aluminum parts with a quality comparable to subtractive manufacturing techniques. Furthermore, the entire additive manufacturing workflow has been digitally integrated, enabling full traceability of all key process steps. This digital integration is critical for ensuring reliable quality assessments of the finished components. T2 - 77th IIW Annual Assembly and International Conference Joint Meeting of C-I, C-IV, and C-XII CY - Rhodos, Greece DA - 07.07.2024 KW - DED-Arc KW - High-strength aluminium alloys KW - Porosity assessment KW - Component testing KW - CT-analyses PY - 2024 AN - OPUS4-62014 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pittner, Andreas T1 - Additive Manufacturing of High Strength Al-Mg-Si Alloys with DED-Arc N2 - Direct energy deposition additive manufacturing technologies that utilize an electric arc have great potential for generating large volume metal components. However, selecting process parameters that yield the desired near net shape design and requested mechanical component behavior is not a trivial task due to the complex relationship between all process parameters and material characteristics. This presentation exemplifies the application of a newly developed solid welding wire doped with TiB to enhance grain refinement in the deposited metal for additive manufacturing based on DED-Arc of high-strength precipitation hardening AlMgSi-aluminum alloys. It is worth noting that the solid wire is the result of our preliminary metallurgical studies on grain refinement in aluminum weld metal. Consequently, research focuses on the correlation between process parameters and component quality to understand the underlying mechanisms. This is crucial for evaluating a robust process parameter space that yields component quality in line with corresponding standards which are mainly taken from welding technology. Specifically, we examine component quality by analyzing pore size and distribution, as well as grain morphology. To enhance the mechanical properties of the deposited metal, a post-weld heat treatment was conducted, comprising of solution treatment, quenching, and artificial aging. The study also evaluates the effects of various heat treatment strategies on the final mechanical properties of the material. To demonstrate the applicability of 3D metal printing of high-strength aluminium alloys, a more complex demonstrator was created. It has been shown that DED-Arc can produce high-volume aluminium parts with the same quality as the corresponding subtractive processing strategy. Additionally, the entire additive manufacturing chain has been digitally integrated, enabling traceability of all relevant process steps, which is essential for reliable subsequent quality assessment. T2 - 4th Symposium on Materials and Additive Manufacturing - Additive 2024 CY - Berlin, Germany DA - 12.06.2024 KW - DED-Arc KW - High-stength aluminium alloys KW - Grain refinement KW - Quality assurance PY - 2024 AN - OPUS4-60248 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Plarre, Rüdiger A1 - Zocca, Andrea A1 - Spitzer, Andrea A1 - Benemann, Sigrid A1 - Gorbushina, Anna A1 - Li, Y. A1 - Waske, Anja A1 - Funk, Alexander A1 - Wilgig, Janka A1 - Günster, Jens T1 - Searching for biological feedstock material: 3D printing of wood particles from house borer and drywood termite frass N2 - Frass (fine powdery refuse or fragile perforated wood produced by the activity of boring insects) of larvae of the European house borer (EHB) and of drywood termites was tested as a natural and novel feedstock for 3D-printing of wood-based materials. Small particles produced by the drywood termite Incisitermes marginipennis and the EHB Hylotrupes bajulus during feeding in construction timber, were used. Frass is a powdery material of particularly consistent quality that is essentially biologically processed wood mixed with debris of wood and faeces. The filigree-like particles flow easily permitting the build-up of woodbased structures in a layer wise fashion using the Binder Jetting printing process. The Quality of powders produced by different insect species was compared along with the processing steps and properties of the printed parts. Drywood termite frass with a Hausner Ratio HR = 1.1 with ρBulk = 0.67 g/cm3 and ρTap = 0.74 g/cm3 was perfectly suited to deposition of uniformly packed layers in 3D printing. We suggest that a variety of naturally available feedstocks could be used in environmentally responsible approaches to scientific material sciences/additive manufacturing. KW - 3D printing KW - X-ray tomographic KW - SEM micrography KW - Drywood termite PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-521517 DO - https://doi.org/10.1371/journal.pone.0246511 VL - 16 IS - 2 SP - e0246511 AN - OPUS4-52151 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Poka, Konstantin A1 - Ali, Sozol A1 - Ulbricht, Alexander A1 - Schröder, Jakob A1 - Khambayat, Jiganesh A1 - Scholz, Maik A1 - Saeed, Waleed A1 - Merz, Benjamin A1 - Epperlein, Martin A1 - Hilgenberg, Kai T1 - Reduction of extraneous variance in powder bed fusion with laser beam of metals by means of advanced digital preprocessing N2 - Data-driven quality assurance and reproducibility are critical for advancing the industrial maturity of Powder Bed Fusion with Laser Beam of Metals (PBF-LB/M). This study addresses the extraneous variance in scan vectors that arises even for identical cross sections of nominally identical components but located at different positions on the build platform. This inherent variance, common across most PBF-LB/M machines, is caused by scan vector computation relative to the machine coordinate origin and subsequent projection of the resulting pattern onto individual component cross sections. In this work, scan vectors are computed still following conventional slicing, but relative to the workpiece origin of each component, using an EOS M 300-4. This digital preprocessing approach homogenizes fabrication conditions, ensuring that anomalies, such as scan vector overlaps, occur consistently across identical components. The impact during fabrication is assessed through powder bed imaging, melt pool monitoring, and operational data from the multilaser PBF-LB/M machine. Components are manufactured from AlSi10Mg for each scan vector computation origin and subsequently qualified using x-ray computed tomography, optical coordinate measurement, and optical surface measurement. A comprehensive evaluation is conducted, comparing the results in terms of component density, geometric accuracy, and surface roughness to those obtained using conventional preprocessing. Based on these findings, practical recommendations are provided, focusing on achieved quality criteria to identify potential drawbacks, while also considering the life cycle analysis of fabrication. Finally, the study emphasizes the significance of consistent scan vector provisioning for identical components placed at different build platform positions, assuming no roll or pitch during nesting. T2 - ICALEO CY - Orlando, FL, USA DA - 13.10.2025 KW - Quality assurance KW - Data management KW - 3D printing KW - Laser fabrication KW - Life cycle analysis PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-642104 DO - https://doi.org/10.2351/7.0001890 SN - 1042-346X VL - 37 IS - 4 SP - 1 EP - 14 PB - American Institute of Physics Publishing CY - New York AN - OPUS4-64210 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 - 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 - JOUR A1 - Przyklenk, A. A1 - Balsamo, A. A1 - O'Connor, D. A1 - Evans, Alexander A1 - Yandayan, T. A1 - Akgöz, S. A1 - Flys, O. A1 - Zeleny, V. A1 - Czułek, D. A1 - Meli, F. A1 - Ragusa, C. A1 - Bosse, H. T1 - New European Metrology Network for advanced manufacturing N2 - Advanced manufacturing has been identified as one of the key enabling technologies with applications in multiple industries. The growing importance of advanced manufacturing is reflected by an increased number of publications on this topic in recent years. Advanced manufacturing requires new and enhanced metrology methods to assure the quality of manufacturing processes and the resulting products. However, a high-level coordination of the metrology community is currently absent in this field and consequently this limits the impact of metrology developments on advanced manufacturing. In this article we introduce the new European Metrology Network (EMN) for Advanced Manufacturing within EURAMET, the European Association of National Metrology Institutes (NMIs). The EMN is intended to be operated sustainably by NMIs and Designated Institutes in close cooperation with Stakeholders interested in advanced manufacturing. The objectives of the EMN are to set up a permanent stakeholder dialogue, to develop a Strategic Research Agenda for the metrology input required for advanced manufacturing technologies, to create and maintain a knowledge sharing programme and to implement a web-based service desk for stakeholders. The EMN development is supported by a Joint Network Project within the European Metrology Programme for Innovation and Research. KW - Stakeholder KW - Advanced manufacturing KW - Metrology KW - European Metrology Networks (EMNs) KW - Strategic Research Agenda (SRA), PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-530618 DO - https://doi.org/10.1088/1361-6501/ac0d25 VL - 32 IS - 11 SP - 111001 PB - IOP Publishing AN - OPUS4-53061 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Rahimi, A. A1 - Coduri, M. A1 - Fitch, A. A1 - Vedani, M. A1 - Schröder, J. A1 - Bruno, Giovanni A1 - Casati, R. T1 - Synchrotron investigation of heat treatment effects on the 3D residual stress distribution in laser powder bed fused AlSi7Mg alloys N2 - For the first time, we utilized synchrotron diffraction to determine 3D maps of residual stresses across the entire volume of thick AlSi7Mg alloy prisms produced by PBF-LB. Samples were subjected to different heat treatment processes: direct aging (T5), solution treatment, and solution treatment followed by aging (T6). The T6 heat treatment resulted in the lowest residual tensile strains, while the T5 treatment resulted in significantly higher residual strains. Maximum residual stresses decreased from the as-built condition to the T6 heat-treated specimen. Although the T5 condition provides greater strength, it also retains higher tensile residual stresses near the surface, which can negatively affect fatigue properties. KW - Neutron Diffraction KW - Residual Stress KW - Large Scale Facilities KW - Additive Manufacturing PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-631089 DO - https://doi.org/10.1016/j.matlet.2025.138742 SN - 0167-577X VL - 396 SP - 1 EP - 5 PB - Elsevier B.V. AN - OPUS4-63108 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 - 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 - CONF A1 - Roveda, Ilaria 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. 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. In order to relax detrimental residual stress and to increase the ductility, post-processing stress relief treatments are performed. The objective of the contribution is to investigate, under different heat treatment condition, 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. T2 - Fatigue Design 2021 CY - Senlis, France DA - 17.11.2021 KW - AlSi10Mg KW - Additive manufacturing KW - L-PBF KW - Residual stress KW - Heat treatment PY - 2021 AN - OPUS4-53794 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Roveda, Ilaria A1 - Mishurova, Tatiana A1 - Evans, Alexander A1 - Fitch, Andrew N. A1 - Haubrich, Jan A1 - Requena, Guillermo A1 - Bruno, Giovanni A1 - Serrano-Munoz, Itziar T1 - Evolution of interphase stress over a crack propagation plane as a function of stress relief heat treatments in a PBF‐LB/M AlSi10Mg alloy N2 - AbstractIn this study, we compare the residual stress state in a laser powder bed fusion (PBF‐LB/M) AlSi10Mg alloy in the as‐built (AB) condition with that after two different heat treatments (265 °C for 1 h, HT1; and 300 °C for 2 h, HT2). The bulk residual stress (RS) is determined using synchrotron X‐ray diffraction (SXRD), and near‐surface profiles are determined using laboratory energy‐dispersive X‐ray diffraction (EDXRD). The EDXRD results do not reveal any notable difference between the conditions at a depth of 350 μm, suggesting that the machining process yields a comparable residual stress state in the near‐surface regions. On the other hand, the SXRD results show that HT1 is more effective in relieving the bulk RS. It is observed that HT1 reduces the RS state in both the aluminium matrix and the silicon network. In addtion, HT2 does not have a significant impact on relaxing the RS as‐built state of the matrix, although it does induce a reduction in the RS magnitudes of the Si phase. It is concluded that the heat treatment stress relieving is effective as long as the Si‐network is not disaggregated. KW - Interphase residual stress KW - Laboratory energy-dispersive X-ray diffraction (EDXRD) KW - PBFLB/M AlSi10Mg alloy KW - Stress-relief heat-treatments KW - Synchrotron X-ray diffraction (SXRD) PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-597591 DO - https://doi.org/10.1111/str.12475 VL - 60 IS - 6 SP - 1 EP - 13 PB - John Wiley & Sons Ltd. AN - OPUS4-59759 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Roveda, Ilaria A1 - Serrano-Munoz, Itziar A1 - Haubrich, J. A1 - Requena, G. A1 - Madia, Mauro T1 - Influence of post-process heat treatments on the fatigue crack propagation behaviour of a PBF-LB/M AlSi10Mg alloy N2 - The microstructure has a great influence on short fatigue crack growth in metallic materials. Laser-based Powder Bed Fusion AlSi10Mg alloys exhibit in the as-built condition a fine fibrous Si structure and a supersaturated solid solution of Si in the α-Al matrix, which is significantly modified by heat treatments starting already at temperatures under 260 °C. This study focuses on the influence of post-process heat treatments on the microstructural evolution and the resulting fatigue crack growth resistance. As compared to the as-built condition, two heat treatments at 265 °C/1 h and at 300 °C/2 h are found to be beneficial to the fatigue crack growth resistance of the investigated material. KW - Additive manufacturing KW - Fatigue crack growth KW - Cyclic R-curve KW - Heat treatment PY - 2023 DO - https://doi.org/10.1016/j.ijfatigue.2023.107808 SN - 0142-1123 VL - 175 SP - 1 EP - 12 PB - Elsevier Ltd. AN - OPUS4-57822 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 - JOUR A1 - Roveda, Ilaria A1 - Serrano-Munoz, Itziar A1 - Mishurova, Tatiana A1 - Madia, Mauro A1 - Pirling, T. A1 - Evans, Alexander A1 - Klaus, M. A1 - Haubrich, J. A1 - Requena, G. A1 - Bruno, Giovanni T1 - Influence of a 265 °C heat treatment on the residual stress state of a PBF-LB/M AlSi10Mg alloy N2 - Laser Powder Bed Fusion (PBF-LB/M) additive manufacturing (AM) induces high magnitude residual stress (RS) in structures due to the extremely heterogeneous cooling and heating rates. As the RS can be deleterious to the fatigue resistance of engineering components, great efforts are focused on understanding their generation and evolution after post-process heat treatments. In this study, one of the few of its kind, the RS relaxation induced in an as-built PBF-LB/M AlSi10Mg material by a low-temperature heat treatment (265 °C for 1 h) is studied by means of X-ray and neutron diffraction. Since the specimens are manufactured using a baseplate heated up to 200 °C, low RS are found in the as-built condition. After heat treatment a redistribution of the RS is observed, while their magnitude remains constant. It is proposed that the redistribution is induced by a repartition of stresses between the a-aluminium matrix and the silicon phase, as the morphology of the silicon phase is affected by the heat treatment. A considerable scatter is observed in the neutron diffraction RS profiles, which is principally correlated to the presence (or absence) of pockets of porosity developed at the borders of the chessboard pattern. KW - Neutron diffraction KW - Additive manufacturing PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-565115 DO - https://doi.org/10.1007/s10853-022-07997-w SN - 1573-4803 VL - 57 SP - 22082 EP - 22098 PB - Springer Science + Business Media CY - Dordrecht AN - OPUS4-56511 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Santi, Alberto A1 - Schröder, Jakob A1 - Serrano-Munoz, Itziar A1 - Bayat, Mohamad A1 - Hattel, Jesper Henri T1 - Exploring the Flash Heating method in additive manufacturing for residual stress prediction: A comparative study with diffraction results from X-ray and neutron techniques N2 - Residual stress (RS) control is crucial for ensuring the performance and reliability of components produced through laser-based powder bed fusion (PBF-LB) additive manufacturing (AM). This study evaluates the Flash Heating (FH) method as an efficient approach for RS prediction, comparing its outcomes with multiple experimental techniques, including X-ray diffraction, neutron diffraction, and layer removal methods. These experimental assessments are conducted in different regions of the component, both before and after detachment from the baseplate. The study validates the FH method and analyzes key numerical parameters, such as meta-layer height, contact time, and time-stepping strategies. Results indicate that FH effectively predicts bulk RS distributions but shows discrepancies in surface stress estimations, likely due to unaccounted factors like surface roughness. Additionally, implementing experimentally derived material properties from as-built AM samples significantly enhances model accuracy compared to conventional material datasets. These findings underscore the potential of FH for efficient RS prediction in PBF-LB while identifying areas for further improvement. Refinements should focus on incorporating anisotropic, temperature-dependent material behavior derived from as-built AM samples and surface roughness effects. This work advances the understanding of key factors necessary for accurate and computationally efficient RS prediction, supporting the optimization of AM processes. KW - Finete element method KW - Inconel 718 KW - Metal additive manufacturing KW - Residual stress KW - Thermomechanics PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-639533 DO - https://doi.org/10.1080/01495739.2025.2541862 SN - 0149-5739 SP - 1 EP - 24 PB - Taylor & Francis AN - OPUS4-63953 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 - CONF A1 - Scheuschner, Nils T1 - In-situ monitoring of the laser powder bed fusion process by thermography, optical tomography and melt pool monitoring for defect detection N2 - For the wide acceptance of the use of additive manufacturing (AM), it is required to provide reliable testing methods to ensure the safety of the additively manufactured parts. A possible solution could be the deployment of in-situ monitoring during the build process. However, for laser powder bed fusion using metal powders (PBF-LB/M ) only a few in-situ monitoring techniques are commercially available (optical tomography, melt pool monitoring) but not researched to an extent that allows to guarantee the adherence to strict quality and safety standards. In this contribution, we present results of a study of PBF-LB/M printed parts made of the nickel-based superalloy Haynes 282. The formation of defects was provoked by local variations of the process parameters and monitored by thermography, optical tomography and melt pool monitoring. Afterwards, the defects were characterized by computed tomography (CT) to identify the detection limits of the used in-situ techniques. T2 - LiM Conference 2023 - Lasers in Manufacturing CY - Munich, Germany DA - 26.06.2023 KW - Thermography KW - High temperature alloys KW - Additive Manufacturing KW - PBF-LB/M PY - 2023 AN - OPUS4-57947 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Scheuschner, Nils T1 - SAMMIE - Research PBF-LB/M system for the development of in-situ monitoring methods N2 - By allowing economic on-demand manufacturing of highly customized and complex workpieces, metal based additive manufacturing (AM) has the prospect to revolutionize many industrial areas. Since AM is prone to the formation of defects during the building process, a fundamental requirement for AM is to find ways to assure the safety and reliability of the additively manufactured parts to become applicable in most fields. A possible solution for this problem lies in the deployment of various in-situ monitoring techniques. However, only a few of these techniques are commercially available and are not researched to an extent that allows to guarantee the adherence to strict quality and safety standards. Since commercial AM machines are not designed for research applications, they provide only limited access to the build chamber during the process and little control over the exact timing and parameters of the process. Therefore, for our research at BAM, we built a laser powder bed fusion system (PBF-LB/M), called “Sensor-based Additive Manufacturing MachInE” (SAMMIE). It provides a fully open system architecture with flexible accesses to the build camber and full control of the complete process. In this contribution, we show first results using thermographic cameras and optical tomography. The flexibility of SAMMIE allows us to use the multiple cameras either fixed relatively to the build plate or coaxially to the process laser. T2 - 20th World Conference on Non-Destructive Testing (WCNDT) CY - Incheon, South Korea DA - 27.05.2024 KW - PBF-LB/M KW - In situ monitoring KW - Custom machine KW - Additive Manufacturing KW - Thermography PY - 2024 AN - OPUS4-62471 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Scheuschner, Nils T1 - Infrared Thermography of the DED-LB/M and PBF LB/M processes N2 - Infrared thermography is a technique that allows to measure the temperatures of objects by analyzing the intensity of the thermal emission without the need of direct contact with very high spatial and temporal resolution. As the temperature is a fundamental factor for the additive manufacturing processes of metals, infrared thermography can provide experimental data that can be used for the validation of simulations and improving the understanding of the processes as well as for in-situ process monitoring for nondestructive evaluation (NDE) for quality control. In this talk we will provide an overview over the possibilities of state of the art thermographic in-situ monitoring systems for the DED-LB/M and PBF-LB/M processes and the challenges such as phase transitions and unknown emissivity values in respect to the determination of real temperatures. We define the requirements for different camera systems in various configurations and give examples on the selection of appropriate measurement parameters and data acquisition techniques as well as on techniques for data analysis and interpretation. Finally, we compare in-situ monitoring methods against post NDE methods by analyzing the advantages and disadvantages of both. This research was funded by BAM within the Focus Area Materials. T2 - Coupled2021 - IX International Conference on Coupled Problems in Science and Engineering CY - Online meeting DA - 13.06.2021 KW - Additive Manufacturing KW - Thermography KW - Direct Energy Deposition PY - 2021 AN - OPUS4-54399 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Scheuschner, Nils A1 - Heinrichsdorff, F. A1 - Oster, Simon A1 - Uhlmann, E. A1 - Polte, J. A1 - Gordei, A. A1 - Hilgenberg, Kai T1 - In-situ monitoring of the laser powder bed fusion process by thermography, optical tomography and melt pool monitoring for defect detection N2 - For the wide acceptance of the use of additive manufacturing (AM), it is required to provide reliable testing methods to ensure the safety of the additively manufactured parts. A possible solution could be the deployment of in-situ monitoring during the build process. However, for laser powder bed fusion using metal powders (PBF-LB/M) only a few in-situ monitoring techniques are commercially available (optical tomography, melt pool monitoring), which have not been researched to an extent that allows to guarantee the adherence to strict quality and safety standards. In this contribution, we present results of a study of PBF-LB/M printed parts made of the nickel-based superalloy Haynes 282. The formation of defects was provoked by local variations of the process parameters and monitored by thermography, optical tomography and melt pool monitoring. Afterwards, the defects were characterized by computed tomography (CT) to identify the detection limits of the used in-situ techniques. T2 - Lasers in Manufacturing Conference 2023 CY - Munich, Germany DA - 26.06.2023 KW - Thermography KW - Optical tomography KW - Melt-pool-monitoring KW - Laser powder bed fusion KW - Haynes 282 KW - Additive Manufacturing PY - 2023 UR - https://www.wlt.de/lim2023-proceedings/system-engineering-and-process-control SP - 1 EP - 10 AN - OPUS4-58466 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Scheuschner, Nils A1 - Oster, Simon A1 - Mohr, Gunther A1 - Hilgenberg, Kai T1 - Comparison of NIR and SWIR thermography for defect detection in Laser Powder Bed Fusion N2 - Since laser powder bed fusion (PBF-LB/M) is prone to the formation of defects during the building process, a fundamental requirement for widespread application is to find ways to assure safety and reliability of the additively manufactured parts. A possible solution for this problem lies in the usage of in-situ thermographic monitoring for defect detection. In this contribution we investigate possibilities and limitations of the VIS/NIR wavelength range for defect detection. A VIS/NIR camera can be based on conventional silicon-based sensors which typically have much higher spatial and temporal resolution in the same price range but are more limited in the detectable temperature range than infrared sensors designed for longer wavelengths. To investigate the influence, we compared the thermographic signatures during the creation of artificially provoked defects by local parameter variations in test specimens made of a nickel alloy (UNS N07208) for two different wavelength ranges (~980 nm and ~1600 nm). KW - Laser powder bed fusion KW - PBF-LB/M KW - Thermography KW - Additive manufacturing KW - NDT PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-610380 DO - https://doi.org/10.1016/j.procir.2024.08.122 VL - 124 SP - 301 EP - 304 PB - Elsevier B.V. AN - OPUS4-61038 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schröder, Jakob T1 - Neutron Diffraction Based Residual Stress Analysis of Additively Manufactured Alloys N2 - Additive manufacturing methods such as laser powder bed fusion offer an enormous flexibility in the efficient design of parts. In this process, a laser locally melts feedstock powder to build up a part layer-by-layer. It is this localized processing manner imposing large temperature gradients, resulting in the formation of internal stress and characteristic microstructures. Produced parts inherently contain high levels of residual stress accompanied by columnar grain growth and crystallographic texture. On a smaller scale, the microstructure is characterized by competitive cell-like solidification with micro segregation and dislocation entanglement. In this context, it is crucial to understand the interplay between microstructure, texture, and residual stress to take full advantage of the freedom in design. In fact, X-ray and neutron diffraction are considered as the benchmark for the non-destructive characterization of surface and bulk residual stress. The latter, characterized by a high penetration power in most engineering alloys, allows the use of diffraction angle close to 90°, enabling the employment of a nearly cubic gauge volume. However, the complex hierarchical microstructures produced by additive manufacturing present significant challenges towards the reliable characterization of residual stress by neutron diffraction. Since residual stress is not the direct quantity being measured, the peak shift imposed by the residual stress present in a material must be converted into a macroscopic stress. First, an appropriate lattice plane must be selected that is easily accessible (i.e., high multiplicity) and insensitive to micro strain accumulation. Second, a stress-free reference must be known to calculate a lattice strain, which can be difficult to define for the heterogeneous microstructures produced by additive manufacturing. Third, an appropriate set of diffraction elastic constants that relate the lattice strain to the macroscopic stress must be known. In this presentation, advancements in the field of residual stress analysis using neutron diffraction are presented on the example of the Ni-based superalloy Inconel 718. The effect of the complex microstructure on the determination of residual stress by neutron diffraction is presented. It is shown, how to deal with the determination of the stress-free reference. It is also shown that the selection of an appropriate set of diffraction elastic constants depends on the microstructure. Finally, the role of the crystallographic texture in the determination of the residual stress is shown. T2 - Deutsche Neutronenstreutagung CY - Aachen, Germany DA - 16.09.2024 KW - Additive Manufacturing KW - Neutron Diffraction KW - Electron Backscatter Diffraction KW - Laser Powder Bed Fusion PY - 2024 AN - OPUS4-61476 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schröder, Jakob T1 - Diffraction and Single-Crystal Elastic Constants of Laser Powder Bed Fused Inconel 718 N2 - Laser powder bed fusion (PBF-LB/M) of metallic alloys is a layer-wise additive manufacturing process that provides significant scope for more efficient designs of components, benefiting performance and weight, leading to efficiency improvements for various sectors of industry. However, to benefit from these design freedoms, knowledge of the high produced induced residual stress and mechanical property anisotropy associated with the unique microstructures is critical. X-ray and neutron diffraction are considered the benchmark for non-destructive characterization of surface and bulk internal residual stress. The latter, characterized by the high penetration power in most engineering alloys, allows for the use of a diffraction angle close to 90° enabling a near cubic sampling volume to be specified. However, the complex microstructures of columnar growth with inherent crystallographic texture typically produced during PBF-LB/M of metallics present significant challenges to the assumptions typically required for time efficient determination of residual stress. These challenges include the selection of an appropriate set of diffraction elastic constants and a representative lattice plane suitable for residual stress analysis. In this contribution, the selection of a suitable lattice plane family for residual stress analysis is explored. Furthermore, the determination of an appropriate set of diffraction and single-crystal elastic constants depending on the underlying microstructure is addressed. In-situ loading experiments have been performed at the Swiss Spallation Neutron Source with the main scope to study the deformation behaviour of laser powder bed fused Inconel 718. Cylindrical tensile bars have been subjected to an increasing mechanical load. At pre-defined steps, neutron diffraction data has been collected. After reaching the yield limit, unloads have been performed to study the accumulation of intergranular strain among various lattice plane families. T2 - 11th European Conference on Residual Stresses CY - Prag, Czech Republic DA - 03.06.2024 KW - Additive Manufacturing KW - Laser Powder Bed fusion KW - Diffraction Elastic Constants KW - Microstructure KW - Electron Backscatter Diffraction PY - 2024 AN - OPUS4-60289 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -