Bernd Wolter

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.

Ferromagnetic rebars and steel strands are widely used to reinforce civil engineering structures but are prone to corrosion-induced defects. According to the World Corrosion Organization, the annual cost estimated of corrosion worldwide is about $2.4 trillion annually, roughly 3 % of world GDP. Assessing defects is vital for determining pipeline fitness-for-service and predicting failure pressures. In this work, we develop a physics-based 3D finite element (FEM) simulation that captures both the nonlinear ferromagnetic behavior and the effects of magnetization frequency in a U-shaped magnetizing yoke. Tangential and normal magnetic fields are computed for conductive and magnetic defects over a range of geometries, lift-offs and excitation conditions. The resulting datasets are then fed into a neural network to accurately infer defect characteristics, such as size and material properties.

Electromagnetic methods for non-destructive evaluation (NDE) are presented, with which sheet metal components can be identified and their material properties can be characterized. The latter is possible with 3MA, the Micromagnetic Multiparametric Microstructure and stress Analyser. This is a combination of several micromagnetic NDE methods that make it possible to analyse the microstructure in a ferromagnetic material and to determine quantitative values of the mechanical material properties or the stress state. In the case of cold forming, the 3MA application for pre-process testing of sheet metal is discussed. Based on the 3MA information, the formability of the sheets can be predicted. To apply 3MA in-line, the influence of the relative speed and the relative distance between the 3MA probe head and the sheet was investigated. In a second study, a spatially resolved eddy current (EC) method was used to create an image of the intrinsic material microstructure of a component for its identification and traceability. It turned out, that these intrinsic fingerprint images can still be recognized even after subsequent plastic deformation or coating of the surface. This enabled the development of a marker-free traceability method for sheet metal processing. It is based on a low-cost array sensor and a specimen identification using robust and partly redundant features of the fingerprint images processed by machine learning (ML).

This study aimed to explore the feasibility of using airborne acoustic emission in laser beam butt welding for the development of an automated classification system based on neural networks. The focus was on monitoring the formation of joint gaps during the welding process. To simulate various sizes of butt joint gaps, controlled welding experiments were conducted, and the emitted acoustic signals were captured using audible-to-ultrasonic microphones. To implement an automated monitoring system, a method based on short-time Fourier transformation was developed to extract audio features, and a convolutional neural network architecture with data augmentation was utilized. The results demonstrated that this non-destructive and non-invasive approach was highly effective in detecting joint gap formations, achieving an accuracy of 98%. Furthermore, the system exhibited promising potential for the low-latency monitoring of the welding process. The classification accuracy for various gap sizes reached up to 90%, providing valuable insights for characterizing and categorizing joint gaps accurately. Additionally, increasing the quantity of training data with quality annotations could potentially improve the classifier model’s performance further. This suggests that there is room for future enhancements in the study.

With increasing power and speed of laser welding, in-process monitoring has become even more crucial to ensure process stability and weld quality. Due to its low cost and installation flexibility, acoustic process monitoring is a promising method and has demonstrated its effectiveness. Since its feasibility has been the focus of existing studies, the temporal resolution of acoustic emissions (AE) has not yet been addressed despite its utmost importance for realizing real-time systems. Aiming to provide a benchmark for further development, this study investigates the relationship between duration and informativeness of AE signals during high power (3.5kW) and high speed (12m/min) laser beam butt welding. Specifically, the informativeness of AE signals is evaluated based on the accuracy of detecting and quantifying joint gaps for various time windows of signals, yielding numerical comparison. The obtained results show that signals can be shortened up to a certain point without sacrificing their informativeness, encouraging to optimize the signal duration. Our results also suggest that large gaps (> 0.3mm) induce unique signal characteristics in AE, which are clearly identifiable from 1 ms signal segments, equivalent to 0.2mm weld seam.