TY - JOUR A1 - Ulbricht, Alexander A1 - Altenburg, Simon A1 - Sprengel, Maximilian A1 - Sommer, Konstantin A1 - Mohr, Gunther A1 - Fritsch, Tobias A1 - Mishurova, Tatiana A1 - Serrano-Munoz, Itziar A1 - Evans, Alexander A1 - Hofmann, M. A1 - Bruno, Giovanni T1 - Separation of the Formation Mechanisms of Residual Stresses in LPBF 316L N2 - Rapid cooling rates and steep temperature gradients are characteristic of additively manufactured parts and important factors for the residual stress formation. This study examined the influence of heat accumulation on the distribution of residual stress in two prisms produced by Laser Powder Bed Fusion (LPBF) of austenitic stainless steel 316L. The layers of the prisms were exposed using two different border fill scan strategies: one scanned from the centre to the perimeter and the other from the perimeter to the centre. The goal was to reveal the effect of different heat inputs on samples featuring the same solidification shrinkage. Residual stress was characterised in one plane perpendicular to the building direction at the mid height using Neutron and Lab X-ray diffraction. Thermography data obtained during the build process were analysed in order to correlate the cooling rates and apparent surface temperatures with the residual stress results. Optical microscopy and micro computed tomography were used to correlate defect populations with the residual stress distribution. The two scanning strategies led to residual stress distributions that were typical for additively manufactured components: compressive stresses in the bulk and tensile stresses at the surface. However, due to the different heat accumulation, the maximum residual stress levels differed. We concluded that solidification shrinkage plays a major role in determining the shape of the residual stress distribution, while the temperature gradient mechanism appears to determine the magnitude of peak residual stresses. KW - Additive Manufacturing KW - Laser Powder Bed Fusion KW - LPBF KW - AISI 316L KW - Online Process Monitoring KW - Thermography KW - Residual Stress KW - Neutron Diffraction KW - X-ray Diffraction KW - Computed Tomography PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-512903 DO - https://doi.org/10.3390/met10091234 VL - 10 IS - 9 PB - MDPI CY - Basel AN - OPUS4-51290 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bruno, Giovanni A1 - Maierhofer, Christiane T1 - Advanced Characterization and On-Line Process Monitoring of Additively Manufactured Materials and Components N2 - Additive manufacturing (AM) techniques have risen to prominence in many industrial sectors. This rapid success of AM is due to the freeform design, which offers enormous possibilities to the engineer, and to the reduction of waste material, which has both environmental and economic advantages. Even safety-critical parts are now being produced using AM. This enthusiastic penetration of AM in our daily life is not yet paralleled by a thorough characterization and understanding of the microstructure of materials and of the internal stresses of parts. The same holds for the understanding of the formation of defects during manufacturing. While simulation efforts are sprouting and some experimental techniques for on-line monitoring are available, still little is known about the propagation of defects throughout the life of a component (from powder to operando/service conditions). This Issue was aimed at collecting contributions about the advanced characterization of AM materials and components (especially at large-scale experimental facilities such as Synchrotron and Neutron sources), as well as efforts to liaise on-line process monitoring to the final product, and even to the component during operation. The goal was to give an overview of advances in the understanding of the impacts of microstructure and defects on component performance and life at several length scales of both defects and parts. KW - Non-destructive Testing KW - Additive Manufacturing KW - Materials Characterization KW - Online Monitoring KW - Residual Stress KW - Thermography KW - Computed Tomography PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-556833 DO - https://doi.org/10.3390/met12091498 VL - 12 IS - 9 SP - 1 EP - 3 PB - MDPI CY - Basel, Schweiz AN - OPUS4-55683 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Averin, Anton T1 - Automated thermographic inspection of radioactive waste drums N2 - In Germany, more than 130,000 cubic meters of low- and medium-level radioactive waste, comprising approximately 90% of the nation’s total radioactive waste, are stored in 200-liter drums. These waste drums, housed in interim storage facilities, are subject to human visual inspections for outer corrosion, while the final disposal site, the Konrad mine, is scheduled for completion in 2030. Manual inspections introduce the risk of human error, making the process less reliable and less safe. The proposed study aims to automate these inspections by implementing remote and non-destructive testing (NDT) methods. The part of project specifically focuses on the application of infrared thermography to detect inner defects in the metallic drums, which could not have been detected so far using visual inspection alone. However, several challenges affect the accuracy of thermographic inspections, such as the presence of surface contaminants (scratches, dirt, stickers), multiple paint layers with low thermal conductivity, and barrel curvature, which disrupt heat distribution and obscure defect signals. This study explores the effectiveness of various thermographic heat sources—flash lamps and laser—as well as techniques including conventional pulse thermography (PT), lock-in thermography. It explores various thermal data processing methods as principal component thermography (PCT), pulse-phase thermography (PPT) and thermal signal reconstruction (TSR). Additionally, machine learning models were estimated to process thermographic images, effectively filtering artifacts (e.g. surface contaminants). The findings suggest that combining advanced thermography techniques with machine learning improves defect detection, ensuring more reliable and automated inspection processes for radioactive waste storage. T2 - SPIE Defense + Commercial Sensing - Thermosense: Thermal Infrared Applications XLVII CY - Orlando, FL, USA DA - 13.04.2025 KW - Automated inspection KW - Defect detection KW - Infrared thermography KW - Laser thermography KW - Machine Learning KW - Principal component thermography KW - Pulse-phase thermography KW - Thermal Data Processing PY - 2025 UR - https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13470/3052732/Automated-thermographic-inspection-of-radioactive-waste-drums/10.1117/12.3052732.short DO - https://doi.org/10.1117/12.3052732 SP - 1 EP - 19 PB - SPIE AN - OPUS4-63285 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Zhu, Pengfei A1 - Maldague, Xavier T1 - Principal Component Analysis-Based Terahertz Self-Supervised Denoising and Deblurring Deep Neural Networks N2 - Terahertz (THz) systems inherently introduce frequency-dependent degradation effects, resulting in low-frequency blurring and high-frequency noise in amplitude images. Conventional image processing techniques cannot simultaneously address both issues, and manual intervention is often required due to the unknown boundary between denoising and deblurring. To tackle this challenge, we propose a principal component analysis (PCA)-based THz self-supervised denoising and deblurring network (THz-SSDD). The network employs a Recorrupted-to-Recorrupted self-supervised learning strategy to capture the intrinsic features of noise by exploiting invariance under repeated corruption. PCA decomposition and reconstruction are then applied to restore images across both low and high frequencies. The performance of the THz-SSDD network was evaluated on four types of samples. Training requires only a small set of unlabeled noisy images, and testing across samples with different material properties and measurement modes demonstrates effective denoising and deblurring. Quantitative analysis further validates the network’s feasibility, showing improvements in image quality while preserving the physical characteristics of the original signals. KW - Non-destructive testing (NDT) KW - Terahertz KW - Denoising KW - Self-supervised learning KW - Deblurring PY - 2026 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-654043 DO - https://doi.org/10.48550/arXiv.2601.12149 SN - 2331-8422 SP - 1 EP - 9 PB - Cornell University CY - Ithaca, NY AN - OPUS4-65404 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kunji Purayil, Sruthi Krishna A1 - Röllig, Mathias A1 - Hirsch, Philipp Daniel A1 - Chaudhuri, Somsubhro A1 - Lecompagnon, Julien A1 - Strobach, L. A1 - Ziegler, Mathias T1 - PCA-enhanced Computational Thermography for the Non-destructive Investigation of the Historic Bücker Bü 181 Aircraft N2 - Infrared thermography is a widely recognized non-destructive testing (NDT) method used in material research and defect detection across various industrial applications. Moreover, thermography plays a crucial role in preserving cultural heritage, including historical paintings and buildings. This study focuses on the application of thermography in inspecting the historic Bücker Bü 181 aircraft, which was used in Germany during World War II. Over time, the original appearance of aircraft has often been altered as part of preservation efforts, either before or during their time in museums, leading to deviations from their historically original state. Additionally, the operational history of such objects is frequently undocumented or entirely lost, making it difficult to understand the presence of artifacts and historically significant data. These factors present major challenges in cultural heritage preservation, and destructive methods cannot be used to investigate such invaluable objects. Therefore, thermography is implemented as a non-destructive and contactless examination method. Active flash thermography combined with phase analysis is a powerful tool for evaluating multilayer systems. In this study, multiple layers of old paint on the object posed a challenge in assessing defect conditions and retrieving other critical information beneath the surface coatings. Nevertheless, pulse thermography not only demonstrated its capability to identify defects and markings in multilayered coatings but also provided insights into the internal structure and subsections of the investigated aircraft. KW - Non-destructive Testing KW - Infrared Thermography KW - Defect Detection KW - Cultural Heritage KW - Multilayer Coatings PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-645709 DO - https://doi.org/10.58286/31934 SN - 2941-4989 VL - 3 IS - 2 SP - 1 EP - 10 PB - NDT.net AN - OPUS4-64570 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hassenstein, Christian A1 - Hirsch, Philipp Daniel A1 - Wassermann, Jonas A1 - Heckel, Thomas T1 - Automated self-adjustment of array probe with a robotic ultrasonic test system N2 - Ultrasonic testing of objects with complex geometries often requires the use of a robotic arm to position the probe perpendicular to the local surface. Using immersion makes it possible to test these objects with standard ultrasonic linear array probes. Here, the probe positions and orientations provided by the robot are used for merging the locally acquired image data into a 3D-reconstruction. The quality of this reconstruction is highly dependent on the alignment of the position of the physical probe with the position used in the digital model. For common industrial tools, the tool center point (TCP) is usually acquired using geometric features of the tools. However, for ultrasonic arrays in immersion, there is a water standoff between the probe and the test object, therefore the TCP is in free space in front of the array and cannot be acquired with the common method. To overcome this challenge, we propose a method that allows the robotic ultrasonic system to automatically self-adjust the position and orientation of the ultrasonic probe using a test block made of steel with defined geometric features as a target for referencing. For each of the six degrees of freedom, a scan and adjustment routine are established using the data based on the actual ultrasound characteristics of the probe. Given a coarse pre-definition of the tool position and the known target test block, minimal human interaction is required to supervise the adjustment method, leading to higher quality reconstructions than with manual adjustment. T2 - 20th World Conference On Non-Destructive Testing (WCNDT 2024) CY - Incheon, South Korea DA - 27.05.2024 KW - Testing KW - Automation KW - NDT 4.0 KW - Robotics KW - Ultrasonic PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-616387 UR - https://www.ndt.net/search/docs.php3?id=30309 SN - 1435-4934 SP - 1 EP - 9 PB - NDT.net AN - OPUS4-61638 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kunji Purayil, Sruthi Krishna T1 - Multimodal Deep Learning Framework for Crack Segmentation in Complex Components Using Robot-Assisted Laser Thermography N2 - Crack detection and segmentation in complex components are critical for maintaining the structural integrity and reliability of systems operating under extreme conditions, such as turbine blades in energy and aerospace applications. The integration of automated multimodal imaging-based non-destructive testing (NDT) with deep learning provides a promising path towards precise and automated defect characterization. In this study, a hybrid multimodal deep learning framework is proposed, combining the advantages of an unsupervised generative adversarial network (GAN) and a supervised U-Net segmentation model for comprehensive crack detection and quantification. The unsupervised multimodal GAN performs data fusion by integrating complementary features from high-resolution thermal and RGB images acquired using a robot-assisted flying laser-line thermography system. This data fusion improves the contrast and representation of surface and sub-surface cracks by leveraging spectral features across multiple imaging modalities. The GAN is trained to reconstruct crack free images and difference between the generated image and real crack image generates an error map that highlights the cracks. The unsupervised approach helps in reducing the need for manual labeled data and generalizes well across different surface conditions. The error maps from GAN are subsequently processed by a U-Net-based segmentation model trained on labeled datasets to achieve precise pixel-level crack localization and morphological estimation. The use of laser thermography induces localized heating on the component surface, providing transient thermal responses that make subtle cracks and defects visible beyond the limits of visual imaging. Experimental validation demonstrates that the proposed hybrid GAN–U-Net framework achieves significantly improved crack detection accuracy and segmentation performance compared to single modal NDE imaging, and data processing based on traditional threshold-based methods. This work underscores the potential of combining unsupervised multimodal fusion with supervised image segmentation to establish a new framework that helps in building automated, data-driven, and robot-assisted NDT systems for intelligent inspection and structural health monitoring of industrial components. T2 - NDE 2025 CY - Mumbai, India DA - 11.12.2025 KW - Non-destructive Testing KW - Infrared Thermography KW - Laser KW - Data Fusion KW - Multimodal Imaging KW - NDE 4.0 PY - 2025 AN - OPUS4-65304 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hirsch, Philipp Daniel T1 - Robotic-assisted 3D scanning and laser thermography for crack inspection on complex components N2 - The integration of automation and robotics into inspection processes has marked a transformative shift in the evaluation of complex components. This study presents a novel approach employing robotic-assisted laser thermography for the automated identification and in-depth analysis of cracks in these intricate structures. This method not only streamlines the inspection process but also eliminates the need for numerous manual steps and the use of chemicals associated with traditional methods such as dye penetrant testing. With the increasing com-plexity of components, this is an important step, especially with regard to additively manufactured components, in order to be able to guarantee component safety for a long lifecycle. T2 - 17th Quantitative InfraRed Thermography Conference (QIRT) CY - Zagreb, Croatia DA - 01.07.2024 KW - Robot KW - Flying line KW - Crack detection KW - Robot path planning KW - Thermography PY - 2024 AN - OPUS4-60914 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lecompagnon, Julien A1 - Ricci, M. A1 - Laureti, S. A1 - Ziegler, Mathias ED - Maldague, X. T1 - Practical study on the thermographic detectability of internal defects using temporally structured laser heating N2 - Modern laser systems have proven to be highly versatile heat sources for active thermographic testing. Compared to more traditional light sources, e.g. flash or halogen lamps, their output power can be easily modulated at high rates, allowing a wide variety of complex excitations to be realized. Although their total optical output power can be theoretically scaled to arbitrary values, the maximum output power is practically limited by many factors: the maximum power that the sample under test can absorb without altering the lighted surface itself, the trade-off between irradiance and inspected area, the cost of the laser system, etc. Furthermore, when working with spatial modulator systems, the output power must be limited to avoid damaging such devices. Nevertheless, to guarantee a sufficient amount of heating even for highly thermally conductive materials and/or deeply buried defects, the heating times can be extended, e.g., either by using step heating, long pulse thermography, or by lock-in thermography with a continuously modulated heating. However, for all these approaches, the ranging capabilities of the thermographic defect detection are reduced due to the limited frequency content of the excitation. To tackle this problem, i.e. to increase the excitation energy while preserving its frequency content, new approaches have been developed in the last two decades, among them the use of coded excitations in combination with pulse-compression, and the use of multiple lock-in analysis or a frequency modulated excitation signal. The challenges of such temporally structured heating techniques are manifold, for example, the DC component inherent in optical heating must be taken into account. In general, a wider frequency bandwidth or greater variability of the frequency components also means greater complexity for signal generation and data processing. In this paper, temporally structured excitation schemes with different degrees of complexity are compared on a high-power laser system. T2 - 17th International Conference on Quantitative InfraRed Thermography 2024 CY - Zagreb, Croatia DA - 01.07.2024 KW - Thermography KW - Laser KW - NDT KW - Coded excitation KW - Defect identification PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-653790 DO - https://doi.org/10.21611/qirt-2024-077 SN - 2371-4085 SP - 1 EP - 9 PB - QIRT Council AN - OPUS4-65379 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hirsch, Philipp Daniel A1 - Kunji Purayil, Sruthi Krishna A1 - Lecompagnon, Julien A1 - Pech May, Nelson Wilbur A1 - Ziegler, Mathias ED - Maldague, X. T1 - Robotic-Assisted 3D Scanning and Laser Thermography for Crack Inspection on Complex Components N2 - The integration of automation and robotics into non-destructive testing (NDT) marks a significant advancement in evaluating complex components. This paper introduces a novel approach using robotic-assisted laser thermography combined with automated 3D scanning to detect and analyze cracks in complex structures. The system uses an integrated line scanner with a robotic arm to capture high-resolution data, creating detailed 3D models for adaptive path planning and precise alignment correction. Laser thermography, based on localized heating and the "flying spot" approach, detects surfacenear cracks with high precision. Crack detection is achieved using the Canny algorithm optional on Fourier-transformed thermograms, offering robust results with minimal computation. This study highlights the potential of robotic-assisted 3D scanning and laser thermography as efficient and precise methods for crack inspection, advancing NDT technologies and ensuring the structural integrity of modern components. T2 - 17th International Conference on Quantitative InfraRed Thermography 2024 CY - Zagreb, Croatia DA - 01.07.2024 KW - Thermography KW - Non-destructive testing KW - Laser line KW - Robotic arm KW - Defect identification KW - Crack detection PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-653809 DO - https://doi.org/10.21611/qirt-2024-078 SP - 1 EP - 8 PB - QIRT Council AN - OPUS4-65380 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -