TY - CONF A1 - Breese, Philipp Peter A1 - Becker, Tina A1 - Oster, Simon A1 - Metz, C. A1 - Altenburg, Simon T1 - In-situ defect detection for laser powder bed fusion with active laser thermography N2 - Defects are still common in metal components built with Additive Manufacturing (AM). Process monitoring methods for laser powder bed fusion (PBF-LB/M) are used in industry, but relationships between monitoring data and defect formation are not fully understood yet. Additionally, defects and deformations may develop with a time delay to the laser energy input. Thus, currently, the component quality is only determinable after the finished process. Here, active laser thermography, a nondestructive testing method, is adapted to PBF-LB/M, using the defocused process laser as heat source. The testing can be performed layer by layer throughout the manufacturing process. We study our proposed testing method along experiments carried out on a custom research PBF-LB/M machine using infrared (IR) cameras. Our work enables a shift from post-process testing of components towards in-situ testing during the AM process. The actual component quality is evaluated in the process chamber and defects can be detected between layers. T2 - 2023 International Solid Freeform Fabrication Symposium CY - Austin, TX, USA DA - 14.08.2023 KW - Additive Manufacturing KW - Laser Powder Bed Fusion KW - Nondestructive Testing KW - Thermography KW - Defect Detection PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-606288 DO - https://doi.org/10.26153/tsw/51096 SP - 1978 EP - 1989 PB - University of Texas at Austin CY - Austin, TX, USA AN - OPUS4-60628 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 - 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 - Altenburg, Simon T1 - Measurement of real temperatures in metal powder bed fusion: Hyperspectral thermography N2 - Detailed knowledge about the physics of the PBF-LB/M process is still lacking, and the simulation of the fast and small-scale process is challenging. Especially the experimental validation of complex simulations lacks a suitable measurement technique for temperature distributions at high speeds and spatial resolution. The complicated process physics, specifically the rapidly changing emissivity in and around the meltpool, pose a severe challenge for usual thermographic approaches. Here, we present first results of a hyperspectral measurement approach to reconstruct temperature and emissivity maps during the PBF-LB/M process in a custom manufacturing machine. The camera setup measures the thermal radiation of the process along a line at a rate of 20 kHz, spectrally resolved between 1 µm and 1.6 µm. When the meltpool travels perpendicularly across this line, a typical meltpool can be reconstructed by pointwise fitting for temperature emissivity separation, based on typical spectral emissivities from reference measurements. T2 - Lasers in Manufacturing Conference - LiM CY - Munich, Germany DA - 23.06.2025 KW - PBF-LB/M KW - In situ monitoring KW - Thermography KW - Additive Manufacturing KW - Process monitoring KW - Hyperspectral PY - 2025 AN - OPUS4-63564 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Breese, Philipp Peter T1 - Fundamentals of quantitative temperature determination during laser powder bed fusion of metals (PBF-LB/M) via hyperspectral thermography N2 - Additive manufacturing (AM, also known as 3D printing) of metals is becoming increasingly important in industrial applications. Reasons for this include the ability to realize complex component designs and the use of novel materials. This distinguishes AM from conventional manufacturing methods such as subtractive manufacturing (turning, milling, etc.). The most widely used AM process for metals is laser powder bed fusion (PBF-LB/M, also known as selective laser melting SLM). Currently, it has the highest degree of industrialization and the largest number of machines in use. In PBF-LB/M, the feedstock is present as metal powder in an inert gas atmosphere inside a process chamber where a laser melts it locally. By repeatedly lowering the build platform, applying a new layer of powder, and then selectively melting it with the laser, a component is built up layer by layer. The local temperature distributions that occur during this process determine not only the properties of the finished component, but also the possible formation of defects such as pores and cracks. Due to the high relevance of the thermal history for precise geometries and defect formation, a temporally and spatially resolved measurement of quantitative (or real/actual) temperatures would be optimal. Quantitative values would ensure comparability and repeatability of the AM process which would also positively affect the quality and safety of the manufactured component. Furthermore, it would also contribute to the validation of simulations and to a deeper understanding of the manufacturing process itself. At present, however, only qualitative monitoring of the thermal radiation is performed (e.g., by monitoring the melt pool using a photodiode), and safety-relevant components must be inspected ex situ afterwards which is time-consuming and costly. A reason for the lack of quantitative temperature data from the process are the challenging conditions of the PBF-LB/M process with high scanning speeds and a small laser spot diameter. Furthermore, the emissivity of the surface changes at high dynamics (temporally/spatially) as well as with temperature and wavelength. This specifically makes contactless temperature determination based on emitted infrared radiation challenging for PBF-LB/M. Although classical thermography offers very good qualitative insights, it is not sufficient for a reliable quantitative temperature determination without a complex temperature calibration including image segmentation and assignment of previously determined emissivities. For this reason, this publication presents the hyperspectral thermography approach for the PBF-LB/M process: The emitted infrared radiation is measured simultaneously at many adjacent wavelengths. In this study, this is realized via a fast hyperspectral line camera that operates in the short-wave infrared range. The thermal radiation of a line on the target is spectrally dispersed and detected to measure the radiant exitance along that line. If the melt pool of the PBF-LB/M process moves through this line at a sufficient frame rate, a spatial reconstruction of an effective melt pool is possible. One approach to determine the desired emissivities and the quantitative temperature from this hyperspectral data are temperature-emissivity separation (TES) methods. A major problem is that n spectral measurements are available, but n+1 parameters are required for each image pixel (n emissivity values + one temperature value). TES methods offer the possibility to approximate this mathematically underconstrained problem in a reliable and traceable way by analytically parameterizing the spectral emissivity with a few degrees of freedom. Using this approach, setup and method are applied to a research machine for PBF-LB/M, called SAMMIE (Sensor-based Additive Manufacturing Machine). First results under AM process conditions are shown which form the basis for the determination of quantitative temperatures in the PBFLB/M process. This marks an important contribution to improving the comparability and repeatability of production, validating simulations, and understanding the process itself. When fully developed and validated, the presented method can also provide reference measurements to evaluate and optimize other, more practical monitoring methods, such as melt pool monitoring or optical tomography. In the long run, this will help to increase confidence in the safety of AM products. T2 - QIRT 2024 CY - Zagreb, Croatia DA - 01.07.2024 KW - Additive Manufacturing KW - Additive Fertigung KW - Real Temperature KW - Melt Pool KW - Emissivity PY - 2024 AN - OPUS4-60762 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Altenburg, Simon T1 - Thermography in laser powder bed fusion of metals: time over threshold as feasible feature in thermographic data N2 - Thermography is one on the most promising techniques for in-situ monitoring for metal additive manufacturing processes. The high process dynamics and the strong focus of the laser beam cause a very complex thermal history within the produced specimens, such as multiple heating cycles within single layer expositions. This complicates data interpretation, e.g., in terms of cooling rates. A quantity that is easily calculated is the time a specific area of the specimen is at a temperature above a chosen threshold value (TOT). Here, we discuss variations occurring in time-over-threshold-maps during manufacturing of a defect free cuboid specimen. T2 - 15th Quantitative InfraRed Thermography conference CY - Online meeting DA - 21.09.2020 KW - Additive Manufacturing KW - Process monitoring KW - Thermography KW - L-PBF KW - Time over threshold PY - 2020 AN - OPUS4-51630 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Mohr, Gunther 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. T2 - 11th CIRP Conference on Photonic Technologies (LANE 2020) CY - Online meeting DA - 07.09.2020 KW - Additive Manufacturing PY - 2020 AN - OPUS4-51255 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Merz, Benjamin T1 - Position Detection for Hybrid Repair of gas turbine blades using PBF-LB/M N2 - This poster presents a workflow for camera-based position detection of components within PBF-LB/M machines. This enables a hybrid repair process of highly stressed components such as gas turbine blades using PBF-LB/M. T2 - Kuratoriumsführung CY - Berlin, Germany DA - 21.06.2022 KW - Additive Manufacturing KW - PBF-LB/M KW - Position detection KW - Camera KW - Image processing PY - 2022 AN - OPUS4-56587 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Gupta, Kanhaiya T1 - Microstructural fingerprinting of additively manufactured components prepared by PBF LB/M N2 - Additive manufacturing (AM) is rapidly emerging from rapid prototyping to industrial production [1]. Thus, providing AM parts with a tagging feature that allows identification, like a fingerprint, can be crucial for logistics, certification, and anti-counterfeiting purposes since nearly any geometry can be produced by AM with stolen data or reverse engineering of an original product. However, the mechanical and functional properties of the replicated part may not be identical to the original ones and pose a safety risk [2]. Several methods are already available, which range from encasing a detector to leveraging the stochastic defects of AM parts for the identification, authentication, and traceability of AM components. The most prevailing solution consists of local process manipulation, such as printing a quick response (QR) code [3] or a set of blind holes on the surface of the internal cavity of hollow components. Local manipulation of components may alter the properties. The external tagging features can be altered or even removed by post-processing treatments. Integrating electronic systems [4] in AM parts can be used to identify and authenticate components with complex or customized geometries. However, metal-based AM, especially in powder bed fusion (PBF-LB/M) techniques, has a strong shielding effect that interferes with the communication between the reader and the transponder. Figure 1: Selection of the few most prominent pores sorted according to decreasing volume that are suitable for tagging and authentication. Our work aims to provide a new methodology for the identification, authentication, and traceability of AM components using microstructural feathers in AM components without altering their properties. Further, we set various benchmark points that can be used in generating the fingerprints for both identification and authentication. This can help digitalize traceability information and tagging features via the link between the physical and cyber worlds through a deeper understanding of the printed object-tag-virtual twin integration. T2 - MSE Konferennz CY - Darmstadt, Germany DA - 24.09.2024 KW - Fingerprint KW - Additive Manufacturing KW - Computed tomography PY - 2024 AN - OPUS4-62286 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -