Bernd Valeske

This work proposes a micromagnetic approach that derives magnetic hysteresis losses and properties from standard 3MA measurements. Non-oriented electrical steel FeSi (M400-50A) and comparative steel 22MnB5 are plastically cold-formed in uniaxial tension and characterized in strain and transverse directions using the 3MA incremental permeability technique. An analytical nonlinear eddy-current model is coupled with a modified Jiles–Atherton hysteresis model, whose parameters are identified from the incremental permeability via an Artificial Bee Colony optimization algorithm. The reconstructed B(H) loops agree well with independent hysteresis measurements and show that plastic deformation increases coercivity, induces pronounced magnetic anisotropy, and improves soft-magnetic behavior of FeSi in one direction. The method enables inline monitoring of forming-induced changes in magnetic properties and provides a basis for minimizing iron losses through process optimization

This study presents a comprehensive 2D transient finite element method (FEM) for modeling incremental permeability in a hybrid 3MA prototype used for non-destructive testing (NDT) of ferromagnetic materials. A dynamic vector Jiles–Atherton (JA) hysteresis model is implemented to capture the nonlinear and hysteretic magnetic behavior of both the specimen and the ferrite yoke, specifically addressing the vector nature of magnetic fields in complex L- and T-shaped yoke–specimen junctions. Classical eddy current effects are incorporated through material electrical conductivity, while the FEM input parameters are identified via an Artificial Bee Colony (ABC) optimization algorithm. The model is validated against experimental measurements for two steel grades 22MnB5 and FeSi, demonstrating excellent agreement for 22MnB5 (relative error < 3%) and good qualitative agreement for FeSi, with residual deviations attributed to unmodeled excess losses. The results confirm the necessity of 2D vector hysteresis models over 1D scalar alternatives for accurate flux distribution prediction in complex geometries, and provide a validated simulation framework for advancing quantitative 3MA based NDT systems.

This work presents a numerical framework for the electromagnetic modeling and inverse characterization of laser-hardened steels using 3MA (Micromagnetic Multiparameter Microstructure and Stress Analysis) non-destructive testing. The proposed methodology combines a simplified two-layer eddy current model, representing the hardened case and the softer core, with the Jiles–Atherton hysteresis model. The associated inverse problem is solved by means of a genetic algorithm, enabling the identification of depth-dependent local hysteresis parameters from measured 3MA incremental permeability signals. The Jiles–Atherton hysteresis parameters are first calibrated using bulk B-H loops. Subsequently, the coupled forward model is used to establish the relationship between these parameters and the measured incremental permeability response for different hardening depths. As a proof of concept, the framework is applied to laser-hardened specimens. The identified local hysteresis and permeability characteristics show clear correlations with both case depth and excitation conditions, demonstrating the potential of the proposed approach for physics-based, non-destructive characterization of laser-hardened layers.

The Fourth Industrial Revolution demands that Non-Destructive Evaluation (NDE) transition from quality assurance to a high-integrity cyber-physical data source (NDE for Industry 4.0). This transformation is critically impeded by a dual failure: a profound deficit of customer confidence in proprietary digitalization solutions, creating significant liability risks, and the crushing economic barrier of non-interoperable data formats. Conventional standardization bodies cannot achieve the necessary IT velocity. The NDE Value Alliance is established as a non-profit association to resolve these systemic failures and provide an immediate path to competitive differentiation. Its mandate has two strategic pillars: 1) developing auditable certification rules to build verifiable trust in NDE 4.0 systems, directly mitigating industrial liability; and 2) architecting the NDE Data Space—foundational infrastructure for open interfaces and data sovereignty. This framework will substantially reduce integration costs, unlock NDE data monetization, and secure NDE's indispensable role in Digital Twins and closed-loop integrity management. Following an April 2026 pre-launch workshop, the Alliance formally launches at ECNDT 2026 in June.

In this work, we present a system architecture that is especially designed for remote ultrasound testing inspections (UT). The system itself is realized using a real-time session service and a web service based on a REST API (REST: Representational State Transfer; API: application programming interface). This web service is used to store data persistently, e.g., sample geometries, raw nondestructive testing (NDT) data and derived inspection results in a shared dataspace. In the current development state, the results consist of textures mapped onto the sample geometry. This approach allows us to display the UT results directly on the real sample using mixed reality technologies. We also implemented a feature to assist the inspector remotely by making use of the availability of this digital representation. Hence, it is necessary to share additional data like the current UT-signal, temporary position marks, user and device positions, etc. between the different participants. A real-time distribution of this highly dynamic data is required to create an effective assistance environment. Therefore, a separate session service is used. The inspection data generated in this temporary session can also be transferred to the afore mentioned dataspace to be saved persistently. The system features mixed reality visualization for the inspector and optionally a virtual reality or a 3D Desktop environment for one or more remote assistants.

Pulsed thermography is an established nondestructive evaluation technology that excels at detecting and characterizing subsurface defects within specimens. A critical challenge in this domain is the accurate estimation of defect depth. In this paper, a new publicly accessible pulsed infrared dataset for PVC specimens is introduced. It was enriched with 3D positional information to advance research in this area. To ensure the labeling quality, a comparative analysis of two distinct data labeling methods was conducted. The first method is based on human domain expertise, while the second method relies on 3D CAD images. The analysis showed that the CAD-based labeling method noticeably enhanced the precision of defect dimension quantification. Additionally, a sophisticated deep learning model was employed on the data, which were preprocessed by different methods to predict both the two-dimensional coordinates and the depth of the identified defects.

Pulsed thermography is an indispensable tool in the field of non-destructive evaluation. However, the data generated by this technique can be challenging to analyze and require expertise to interpret. With the rapid progress in deep learning, image segmentation has become a well-established area of research. This has motivated efforts to apply deep learning methods to non-destructive evaluation data processing, including pulsed thermography. Despite this trend, there has been a lack of public pulsed thermography datasets available for the evaluation of various spatial-temporal deep learning models for segmentation tasks. This paper aims to address this gap by presenting the PVC-Infrared dataset for deep learning. In addition, we evaluated the performance of popular deep-learning-based instance segmentation models on this dataset. Furthermore, we examined the effect of the number of frames and data transformations on the performance of these models. The results of this study suggest that appropriate preprocessing techniques can significantly reduce the size of the data while maintaining the performance of deep learning models, thereby speeding up the data processing process. This highlights the potential for using deep learning methods to make non-destructive evaluation data analysis more efficient and accessible to a wider range of practitioners.

Pulsed thermography is a vital technique in the nondestructive evaluation field. However, its data analysis can be complex and requires skilled experts. Advances in deep learning have yielded exceptional results, including image segmentation. Therefore, many efforts have been made to apply deep learning methods to data processing for nondestructive evaluation. Despite this, there is currently no public Pulsed thermographic dataset available for evaluating various spatial-temporal deep methods of segmenting pulsed thermographic data. This article aims to provide such a dataset and assess the performance of commonly used deep learning-based instance segmentation models on it. Additionally, the impact of the number of frames and data transformations on model performance is examined. The findings suggest that suitable preprocessing methods can effectively reduce the data size without compromising the deep models’ performance.