TY - CONF A1 - Botsch, B T1 - Classification of fracture surface types based on SEM images N2 - The following work deals with the quantitative fracture surface evaluation in damage analysis. So far, fracture surfaces have almost exclusively been evaluated qualitatively, i.e. the presence of fracture features is documented and their surface proportions are estimated, if necessary. Many years of experience are required, as well as an intensive comparison with defined comparative images from the literature. The aim of this work is the development of classifiers which can recognize fracture mechanisms or fracture features in scanning electron microscope images (SEM). The basis is 46 SEM images, which have been evaluated by fractography experts with regard to fracture features. The existing data set of images is expanded using augmentation methods in order to increase the variability of the data and counteract overfitting. Only convolutional neural networks (CNN) are used to create the classifiers. Various network configurations are tested, with the SegNet achieving the best results. T2 - Materialsweek 2021 CY - Online meeting DA - 07.09.2021 KW - Fractography KW - Fracture surface KW - Deep learning KW - SEM PY - 2021 AN - OPUS4-53418 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kunji Purayil, Sruthi Krishna A1 - Balasubramaniam, K. T1 - Data-driven AI for the automated classification of the isothermal heat-treated thermal barrier coatings using pulsed infrared thermography N2 - Development of reliable age prediction models are crucial in monitoring the formation of oxide layer and degradation of TBC at regular intervals. This study proposes an automated classification of isothermal heat-treated TBC samples using temperature data, which helps in predicting the TBC life and monitoring the TBC degradation. TBC-coated samples are isothermal heat-treated at 1000 °C, and the initial growth of thermally grown oxide is monitored using a non-destructive thermal imaging technique. The proposed study integrates data-driven AI (DAI) models and feature extraction techniques to interpret complex thermal patterns measured from the TBC coating surface. The performance of the proposed classification framework is tested using deep learning and classical machine learning models with different types and window sizes of input data. Input data used for validation are raw experiment data, logarithmic of experiment data, polynomial fit data, and thermal signal reconstruction fit coefficients. The maximum c KW - Infrared thermography KW - NDT KW - Artificial intelligence KW - Deep learning KW - Predictive maintenance PY - 2024 DO - https://doi.org/10.1088/1361-6463/ad8ce7 SN - 1361-6463 VL - 58 IS - 4 SP - 1 EP - 10 PB - IOP Publishing CY - Bristol AN - OPUS4-61641 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Javaheri, E. A1 - Kumala, V. A1 - Javaheri, A. A1 - Rawassizadeh, R. A1 - Lubritz, J. A1 - Graf, B. A1 - Rethmeier, Michael T1 - Quantifying Mechanical Properties of Automotive Steels with Deep Learing Based Computer Vision Algorithms N2 - This paper demonstrates that the instrumented indentation test (IIT), together with a trained artificial neural network (ANN), has the capability to characterize the mechanical properties of the local parts of a welded steel structure such as a weld nugget or heat affected zone. Aside from force-indentation depth curves generated from the IIT, the profile of the indented surface deformed after the indentation test also has a strong correlation with the materials’ plastic behavior. The profile of the indented surface was used as the training dataset to design an ANN to determine the material parameters of the welded zones. The deformation of the indented surface in three dimensions shown in images were analyzed with the computer vision algorithms and the obtained data were employed to train the ANN for the characterization of the mechanical properties. Moreover, this method was applied to the images taken with a simple light microscope from the surface of a specimen. Therefore, it is possible to quantify the mechanical properties of the automotive steels with the four independent methods: (1) force-indentation depth curve; (2) profile of the indented surface; (3) analyzing of the 3D-measurement image; and (4) evaluation of the images taken by a simple light microscope. The results show that there is a very good Agreement between the material parameters obtained from the trained ANN and the experimental uniaxial tensile test. The results present that the mechanical properties of an unknown steel can be determined by only analyzing the images taken from its surface after pushing a simple indenter into its surface. KW - Deep learning KW - Computer vision KW - Artificial neural network KW - Clustering KW - Mechanical properties KW - High strength steels KW - Instumented indentation test PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-503038 DO - https://doi.org/10.3390/met10020163 VL - 10 IS - 2 SP - 163 PB - MDPI AN - OPUS4-50303 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CHAP A1 - Chand, Keerthana A1 - Fritsch, Tobias A1 - Hejazi, Bardia A1 - Poka, Konstantin A1 - Bruno, Giovanni T1 - Deep Learning Based 3D Volume Correlation for Additive Manufacturing Using High-Resolution Industrial X-Ray Computed Tomography N2 - Quality control in Additive Manufacturing (AM) is vital for industrial applications in areas such as the automotive, medical, and aerospace sectors. Geometric inaccuracies caused by shrinkage and deformations can compromise the life and performance of additively manufactured components. Such deviations can be quantified using Digital Volume Correlation (DVC), which compares the Computer-Aided Design (CAD) model with the X-ray Computed Tomography (XCT) geometry of the components produced. However, accurate registration between the two modalities is challenging due to the absence of a ground truth or reference deformation field. In addition, the extremely large data size of high-resolution XCT volumes makes computation difficult. In this work, we present a deep learning-based approach for estimating voxel-wise deformations between CAD and XCT volumes. Our method uses a dynamic patch-based processing strategy to handle high-resolution volumes. In addition to the Dice score, we introduce a Binary Difference Map (BDM) that quantifies voxel-wise mismatches between binarized CAD and XCT volumes to evaluate the accuracy of the registration. Our approach shows a 9.2% improvement in the Dice score and a 9.9% improvement in the voxel match rate compared to classic DVC methods, while reducing the interaction time from days to minutes. This work sets the foundation for deep learning-based DVC methods to generate compensation meshes that can then be used in closed-loop correlations during the AM production process. Such a system would be of great interest to industry, as it would make the manufacturing process more reliable and efficient, saving time and material. KW - Deep learning PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-646293 DO - https://doi.org/10.3233/FAIA251475 SN - 0922-6389 VL - 413 SP - 5368 EP - 5375 PB - IOS Press AN - OPUS4-64629 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Evsevleev, Sergei T1 - Application of deep learning to the segmentation of synchrotron X-ray tomography data of multiphase metal matrix composites N2 - The 3D microstructure of an Al alloy matrix composite with two ceramic reinforcements was investigated by synchrotron X-ray tomography. A deep learning algorithm was used for the segmentation of four different phases. We show that convolutional networks with the U-Net architecture are able to solve complex segmentation tasks with small amount of training data. T2 - International Conference on Tomography of Materials & Structures CY - Cairns, Australia DA - 22.07.2019 KW - Synchrotron X-ray tomography KW - Deep learning KW - Segmentation KW - Metal matrix composite PY - 2019 AN - OPUS4-48606 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Hejazi, Bardia A1 - Compart, Amaya A1 - Fritsch, Tobias A1 - Wagner, Ruben A1 - Weidner, Anja A1 - Biermann, Horst A1 - Benz, Christopher A1 - Sander, Manuela A1 - Bruno, Giovanni T1 - Fatigue Crack Segmentation and Characterization of Additively Manufactured Ti‐6Al‐4V Using X‐Ray Computed Tomography N2 - X‐ray computed tomography (XCT) is extremely useful for the non‐destructive analysis of additively manufactured (AM) components. AM components often show manufacturing defects such as lack‐of‐fusion (LoF), which are detrimental to the fatigue life of components. To better understand how cracks initiate and propagate from internal defects, we fabricated Ti‐6Al‐4V samples with an internal cavity using electron beam powder bed fusion. The samples were tested in high‐cycle and very high‐cycle fatigue regimes. XCT was used to locate crack initiation sites and to determine characteristic properties of cracks and defects with the aid of deep learning segmentation. LoF defects exposed to the outer surface of the samples after machining were found to be as detrimental to fatigue life as the internal artificial defects. This work can benefit industries that utilize the AM of high‐strength, lightweight alloys, in the design and manufacturing of components to improve part reliability and fatigue life. KW - Additive manufacturing KW - Deep learning KW - Ttitanium alloy KW - Very high-cycle fatigue KW - X-ray computed tomography PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-615107 DO - https://doi.org/10.1111/ffe.14489 SP - 1 EP - 13 PB - Wiley AN - OPUS4-61510 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hejazi, Bardia A1 - Fritsch, Tobias A1 - Benz, Christopher A1 - Radtke, Lars A1 - Sander, Manuela A1 - Bruno, Giovanni T1 - In-situ very high cycle fatigue experiments of additively manufactured Ti-6Al-4V using X-ray computed tomography N2 - X-ray computed tomography (XCT) is an invaluable method for evaluating the properties and performance of components both during service and after failure in a non-destructive manner. XCT is particularly useful for the investigation of additively manufactured (AM) components, which often have production defects that are inherent to the manufacturing process, such as lack of fusion defects. Understanding the mechanisms of fatigue crack growth throughout the life cycle of such components is crucial and so to address this need, we designed and performed experiments to investigate the fatigue life and fatigue crack growth behavior of Ti-6Al-4V components under very high cycle fatigue (VHCF) testing. The titanium samples were additively manufactured with intentional internal defects to control crack initiation location. XCT of the component was carried out to identify crack initiation sites and characterize the dynamics of crack growth. The findings from this work will benefit industries that rely on the AM of titanium alloys, aiding in the improvement of component design and manufacturing processes. T2 - Alloys for additive manufacturing 2025 (AAMS 2025) CY - Neuchâtel, Switzerland DA - 02.09.2025 KW - X-ray computed tomography KW - Deep learning KW - Titanium alloy KW - Very high-cycle fatigue PY - 2025 DO - https://doi.org/10.5281/zenodo.15261296 AN - OPUS4-64096 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -