TY - JOUR A1 - Miller, Eddi A1 - Ceballos, Hector A1 - Engelmann, Bastian A1 - Schiffler, Andreas A1 - Batres, Rafael A1 - Schmitt, Jan T1 - Industry 4.0 and International Collaborative Online Learning in a Higher Education Course on Machine Learning JF - 2021 Machine Learning-Driven Digital Technologies for Educational Innovation Workshop Y1 - 2021 SP - 1 EP - 8 ER - TY - CHAP A1 - Wehnert, Kira-Kristin A1 - Schäfer, S A1 - Schmitt, Jan A1 - Schiffler, Andreas T1 - C7. 4 Application of Laser Line Scanners for Quality Control during Selective Laser Melting (SLM) T2 - SMSI 2021-System of Units and Metreological Infrastructure Y1 - 2021 SP - 298 EP - 299 ER - TY - JOUR A1 - Lang, Silvio A1 - Engelmann, Bastian A1 - Schiffler, Andreas A1 - Schmitt, Jan T1 - A simplified machine learning product carbon footprint evaluation tool JF - Cleaner Environmental Systems N2 - On the way to climate neutrality manufacturing companies need to assess the Carbon dioxide (CO2) emissions of their products as a basis for emission reduction measures. The evaluate this so-called Product Carbon Footprint (PCF) life cycle analysis as a comprehensive method is applicable, but means great effort and requires interdisciplinary knowledge. Nevertheless, assumptions must still be made to assess the entire supply chain. To lower these burdens and provide a digital tool to estimate the PCF with less input parameter and data, we make use of machine learning techniques and develop an editorial framework called MINDFUL. This contribution shows its realization by providing the software architecture, underlying CO2 factors, calculations and Machine Learning approach as well as the principles of its user experience. Our tool is validated within an industrial case study. KW - Management, Monitoring, Policy and Law KW - Environmental Science (miscellaneous) KW - Renewable Energy, Sustainability and the Environment KW - Environmental Engineering Y1 - 2024 U6 - https://doi.org/10.1016/j.cesys.2024.100187 SN - 2666-7894 VL - 13 PB - Elsevier BV ER - TY - JOUR A1 - Höfflin, Dennis A1 - Sauer, Christian A1 - Schiffler, Andreas A1 - Versch, Alexander A1 - Hartmann, Jürgen T1 - Active thermography for in-situ defect detection in laser powder bed fusion of metal JF - Journal of Manufacturing Processes N2 - Additive manufacturing (AM) has revolutionized production by offering design flexibility, reducing material waste, and enabling intricate geometries that are often unachievable with traditional methods. As the use of AM for metals continues to expand, it is crucial to ensure the quality and integrity of printed components. Defects can compromise the mechanical properties and performance of the final product. Non-destructive testing (NDT) techniques are necessary to detect and characterize anomalies during or post-manufacturing. Active thermography, a thermal imaging technique that uses an external energy source to induce temperature variations, has emerged as a promising tool in this field. This paper explores the potential of in-situ non-destructive testing using the processing laser of a PBF-LB/M setup as an excitation source for active thermography. With this technological approach, artificially generated internal defects underneath an intact surface can be detected down to a defect size of 350 μm – 450 μm. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:863-opus-57601 SN - 1526-6125 VL - 131 SP - 1758 EP - 1769 PB - Elsevier BV ER - TY - JOUR A1 - Höfflin, Dennis A1 - Sauer, Christian A1 - Schiffler, Andreas A1 - Manara, Jochen A1 - Hartmann, Jürgen T1 - Pixelwise high-temperature calibration for in-situ temperature measuring in powder bed fusion of metal with laser beam JF - Heliyon N2 - High-temperature calibration methods in additive manufacturing involve the use of advanced techniques to accurately measure and control the temperature of the build material during the additive manufacturing process. Infrared cameras, blackbody radiation sources and non-linear optimization algorithms are used to correlate the temperature of the material with its emitted thermal radiation. This is essential for ensuring the quality and repeatability of the final product. This paper presents the calibration procedure of an imaging system for in-situ measurement of absolute temperatures and temperature gradients during powder bed fusion of metal with laser beam (PBF-LB/M) in the temperature range of 500 K–1500 K. It describes the design of the optical setup to meet specific requirements in this application area as well as the procedure for accounting the various factors influencing the temperature measurement. These include camera-specific effects such as varying spectral sensitivities of the individual pixels of the sensor as well as influences of the exposure time and the exposed sensor area. Furthermore, influences caused by the complex optical path, such as inhomogeneous transmission properties of the galvanometer scanner as well as angle-dependent transmission properties of the f-theta lens were considered. A two-step fitting algorithm based on Planck's law of radiation was applied to best represent the correlation. With the presented procedure the calibrated thermography system provides the ability to measure absolute temperatures under real process conditions with high accuracy. Y1 - 2024 U6 - https://doi.org/10.1016/j.heliyon.2024.e28989 SN - 2405-8440 VL - 10 IS - 7 PB - Elsevier BV ER - TY - JOUR A1 - Martinez, Mario A1 - Schmitt, Anna-Maria A1 - Schiffler, Andreas A1 - Engelmann, Bastian T1 - Production Data Set for five-Axis CNC Milling with multiple Changeovers JF - Scientific Data N2 - Abstract This data descriptor contains information about an extensive production data set for a five-axis CNC milling process. Three geometrically different products were manufactured and relevant features from the numerical control of the machine were recorded. The recorded manufacturing process contains the preparation of the machine for the next product (changeover) as well as the machining process (production). The experimental manufacturing was organized with the aid of a changeover matrix to ensure that all possible changeover combinations for the three products were considered. The production was repeated five times, resulting in 30 manufacturing sessions and five complete changeover matrices. The data set was recorded in a laboratory environment. A rich feature set including i.e. the NC-code of the products, tool information, and a Jupyter notebook is provided with the data set. Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1038/s41597-025-05294-0 SN - 2052-4463 VL - 12 IS - 1 PB - Springer Science and Business Media LLC ER - TY - GEN A1 - Höfflin, Dennis A1 - Schiffler, Andreas A1 - Hartmann, Jürgen A1 - Sauer, Christian T1 - Dual Scan head approach for in-situ defect detection in laser powder bed fusion of metals - Dataset N2 - This dataset contains thermographic data from a study on in-situ defect detection in the Laser Powder Bed Fusion of Metals (PBF-LB/M) process. The data was collected using a novel experimental setup named Synchronized Path Infrared Thermography (SPIT), which employs a dual scan head configuration. One scan head directs the processing laser, while the second scan head positions the measurement field of an infrared (IR) camera. This setup allows for the precise analysis of the cooling behavior of the material decoupled from the immediate laser-material interaction zone. The experiments were conducted on pre-fabricated stainless steel (EOS StainlessSteel PH1, DIN 14540) samples with embedded, cylindrical subsurface defects of varying diameters. A single layer of metal powder was applied to these samples and then fused by the laser. The dataset includes a series of measurements where process parameters, specifically the volumetric energy density and the laser scanning speed, were systematically varied to assess their influence on defect detection reliability. The provided data consists of raw thermographic recordings, which capture the surface temperature distribution in the heat-affected zone behind the melt pool. These recordings can be used to identify localized areas of elevated temperature caused by the insulating effect of the subsurface defects, which impede heat transfer into the substrate. This dataset is valuable for researchers working on process monitoring, defect detection algorithms, and the validation of thermal simulations in additive manufacturing. Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.15727369 ER - TY - GEN A1 - Sauer, Christian A1 - Schiffler, Andreas A1 - Höfflin, Dennis A1 - Hartmann, Jürgen T1 - Temporally Gated Active Thermography for Defect Detection in Laser-Based Powder Bed Fusion of Metals - Dataset N2 - This HDF5-dataset contains in-situ high-speed infrared thermography data acquired during the Laser-Based Powder Bed Fusion (PBF-LB/M) process. The data was collected using a Synchronized Path Infrared Thermography (SPIT) setup, which employs a dual-scanhead configuration to guide both the processing laser and the thermal camera's field of view. The primary feature of this dataset is the application of a temporally gated acquisition strategy. The infrared camera's integration time (800 µs) was synchronized with a modulated processing laser (500 Hz) to capture thermal data exclusively during the laser-off phases. This method effectively isolates the material's thermal emission from high-intensity laser reflections. Y1 - 2025 U6 - https://doi.org/10.5281/zenodo.17747278 ER - TY - JOUR A1 - Höfflin, Dennis A1 - Hartmann, Jürgen A1 - Rosilius, Maximilian A1 - Seitz, Philipp A1 - Schiffler, Andreas T1 - Opto-Thermal Investigation of Additively Manufactured Steel Samples as a Function of the Hatch Distance JF - Sensors N2 - Nowadays, additive manufacturing processes are becoming more and more appealing due to their production-oriented design guidelines, especially with regard to topology optimisation and minimal downstream production depth in contrast to conventional technologies. However, a scientific path in the areas of quality assurance, material and microstructural properties, intrinsic thermal permeability and dependent stress parameters inhibits enthusiasm for the potential degrees of freedom of the direct metal laser melting process (DMLS). Especially in quality assurance, post-processing destructive measuring methods are still predominantly necessary in order to evaluate the components adequately. The overall objective of these investigations is to gain process knowledge make reliable in situ statements about component quality and material properties based on the process parameters used and emission values measured. The knowledge will then be used to develop non-destructive tools for the quality management of additively manufactured components. To assess the effectiveness of the research design in relation to the objectives for further investigations, this pre-study evaluates the dependencies between the process parameters, process emission during manufacturing and resulting thermal diffusivity and the relative density of samples fabricated by DMLS. Therefore, the approach deals with additively built metal samples made on an EOS M290 apparatus with varying hatch distances while simultaneously detecting the process emission. Afterwards, the relative density of the samples is determined optically, and thermal diffusivity is measured using the laser flash method. As a result of this pre-study, all interactions of the within factors are presented. The process variable hatch distance indicates a strong influence on the resulting material properties, as an increase in the hatch distance from 0.11 mm to 1 mm leads to a drop in relative density of 57.4%. The associated thermal diffusivity also reveals a sharp decrease from 5.3 mm2/s to 1.3 mm2/s with growing hatch distances. The variability of the material properties can also be observed in the measured process emissions. However, as various factors overlap in the thermal radiation signal, no clear assignment is possible within the scope of this work. KW - additive manufacturing processes KW - material Y1 - 2021 U6 - https://doi.org/10.3390/s22010046 SN - 1424-8220 VL - 22 IS - 1 PB - MDPI ER - TY - JOUR A1 - Schiffler, Andreas A1 - Wehnert, Kira-Kristin A1 - Ochs, Dennis T1 - Einsatz einer maschinell gelernten Bildsegmentierung zur Pulverbettüberwachung im Metalldruck JF - FHWS Science Journal N2 - Der Schwerpunkt der folgenden Ausführungen ist auf eine schichtweise Erkennung von Abweichungen durch die automatisierte Analyse von Bilddaten aus pulverbettbasierten Metalldruckprozessen gelegt. Bei diesen Prozessen wird eine dünne Schicht im Bereich von 20 bis 100 μm aus pulverförmigem Metallpulver aufgetragen. Ein zweidimensionaler Querschnitt des gewünschten Bauteils wird dann entweder mit einer selektiven Wärmequelle aufgeschmolzen oder mit einem Bindemittel zusammengebunden. Anschließend wird das Substrat um die Höhe einer Pulverschicht abgesenkt und der Vorgang wiederholt, bis der Aufbau abgeschlossen ist. Nach dem Abschluss des Aufschmelzens einer Schicht wird ein Bild mittels einer Kamera im sichtbaren Wellenlängenbereich erstellt. Abbildung 1 zeigt zwei Beispiele solcher Bilder. Diese bilden die Eingangsgröße für die Erkennung von Abweichungen. Durch die gewählte Schichtdicke kann die Herstellung eines Bauteils mehrere tausend Bilder erzeugen. Die automatisierte und zeitnahe Auswertung ist daher Inhalt aktueller Forschungs- und Entwicklungsaktivitäten [1]. Nicht zuletzt da die notwendige Sensorik – eine Kamera – wirtschaftlich und robust einsetzbar ist. KW - bilddaten KW - bilderkennung KW - metalldruck Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:863-opus-20021 UR - https://nbn-resolving.org/urn:nbn:de:bvb:863-opus-19389 SN - 2196-6095 VL - 5 IS - 2 SP - 147 EP - 152 ER - TY - JOUR A1 - Miller, Eddi A1 - Barthelme, Christine A1 - Schiffler, Andreas A1 - Engelmann, Bastian A1 - Schmitt, Jan T1 - Internationalisierung in Pandemiezeiten, technische Möglichkeiten, Lehr- und Forschungskonzepte mal anders gedacht JF - FHWS Science Journal N2 - Eines der zentralen strategischen Ziele unserer Hochschule ist die Internationalisierung, sowie der »internationalisation@home«. Als die weltweite Corona-Pandemie die Präsenzlehre und -forschung ebenso wie den internationalen Austausch von Studierenden und Forschenden zu Beginn 2020 quasi zum Erliegen brachte wurden die Rufe nach digitalen Angeboten im internationalen Bereich schnell laut. Vor diesem Hintergrund reagierte der »Deutsche Akademische Auslandsdienst (DAAD)« mit der kurzfristig ins Leben gerufenen Förderlinie »International Virtual Academic Collaboration« (IVAC), um internationale Hochschulkooperationen und weltweite Mobilität unter digitalen Vorzeichen strategisch zu gestalten und auszubauen [1]. KW - internationalisierung KW - covid KW - corona Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:863-opus-20035 UR - https://nbn-resolving.org/urn:nbn:de:bvb:863-opus-19389 SN - 2196-6095 VL - 5 IS - 2 SP - 143 EP - 146 ER - TY - JOUR A1 - Wehnert, Kira-Kristin A1 - Ochs, Dennis A1 - Schmitt, Jan A1 - Hartmann, Jürgen A1 - Schiffler, Andreas T1 - Reducing Lifecycle Costs due to Profile Scanning of the Powder Bed in Metal Printing JF - Procedia CIRP 98 N2 - First time right is one major goal in powder based 3D metal printing. Reaching this goal is driven by reducing life cycle costs for quality measures, to minimize scrap rate and to increase productivity under optimal resource efficiency. Therefore, monitoring the state of the powder bed for each printed layer is state of the art in selective laser melting. In the most modern approaches the quality monitoring is done by computer vision systems working with an interference on trained neural networks with images taken after exposure and after recoating. There are two drawbacks of this monitoring method: First, the sensor signals - the image of the powder bed - give no direct height information. Second, the application of this method needs to be trained and labeled with reference images for several cases. The novel approach presented in this paper uses a laser line scanner attached to the recoating machine. With this new concept, a direct threshold measure can be applied during the recoating process to detect deviations in height level without prior knowledge. The evaluation can be done online during recoating and feedback to the controller to monitor each individual layer. Hence, in case of deviations the location in the printing plane is an inherent measurement and will be used to decide which severity of error is reported. The signal is used to control the process, either by starting the recoating process again or stopping the printing process. With this approach, the sources of error for each layer can be evaluated with deep information to evaluate the cause of the error. This allows a reduction of failure in the future, which saves material costs, reduces running time of the machine life cycle phase in serial production and results in less rework for manufactured parts. Also a shorter throughput time per print job results, which means that the employee can spent more time to other print jobs and making efficient use of the employee’s work force. In summary, this novel approach will not only reduce material costs but also operating costs and thus optimize the entire life cycle cost structure. The paper presents a first feasibility and application of the described approach for test workpieces in comparison to conventional monitoring systems on an EOS M290 machine. Y1 - 2021 UR - 10.1016/j.procir.2021.01.175 VL - 98 SP - 684 EP - 689 PB - Elsevir ER - TY - JOUR A1 - Schmitt, Anna-Maria A1 - Sauer, Christian A1 - Höfflin, Dennis A1 - Schiffler, Andreas T1 - Powder Bed Monitoring Using Semantic Image Segmentation to Detect Failures during 3D Metal Printing JF - Sensors N2 - Monitoring the metal Additive Manufacturing (AM) process is an important task within the scope of quality assurance. This article presents a method to gain insights into process quality by comparing the actual and target layers. Images of the powder bed were captured and segmented using an Xception–style neural network to predict the powder and part areas. The segmentation result of every layer is compared to the reference layer regarding the area, centroids, and normalized area difference of each part. To evaluate the method, a print job with three parts was chosen where one of them broke off and another one had thermal deformations. The calculated metrics are useful for detecting if a part is damaged or for identifying thermal distortions. The method introduced by this work can be used to monitor the metal AM process for quality assurance. Due to the limited camera resolutions and inconsistent lighting conditions, the approach has some limitations, which are discussed at the end. KW - additive manufacturing KW - metal printing KW - neural network KW - semantic segmentation KW - thermal distortion KW - in situ monitoring Y1 - 2023 U6 - https://doi.org/10.3390/s23094183 VL - 23 IS - 9 SP - 4183 EP - 4183 PB - MDPI ER - TY - GEN A1 - Seybold, Alexander A1 - Wegner, Christoph A1 - Glück, Stefan A1 - Schiffler, Andreas A1 - Voll, Martin T1 - Measuring system for monitoring a spindle (Patent, US-20210187684-A1) Y1 - 2021 UR - https://image-ppubs.uspto.gov/dirsearch-public/print/downloadPdf/20210187684 ER - TY - CHAP A1 - Lutz, Benjamin A1 - Kisskalt, Dominik A1 - Regulin, Daniel A1 - Reisch, Raven A1 - Schiffler, Andreas A1 - Franke, Jörg T1 - Evaluation of deep learning for semantic image segmentation in tool condition monitoring T2 - 2019 18th IEEE international conference on machine learning and applications (ICMLA) Y1 - 2019 SP - 2008 EP - 2013 ER - TY - JOUR A1 - Schiffler, Andreas A1 - Runde, Stefan T1 - CNC-Shopfloor-Management Ein neuer Weg zum optimalen NC-Programm JF - Digital Manufactoring Y1 - 2019 IS - 03/2019 SP - 10 EP - 14 ER -