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.

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.

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.

In this paper, a method for determining the filling level of grooves (1 mm (W) × 0.25 mm (H)) in pressed titanium pipe-fitting joints is presented. The joints are inspected in a water bath using a 20 MHz phased array ultrasound, and the acquired raw B-scans are evaluated by a convolutional neural network that performs per-groove regression. Reference filling levels are obtained destructively from micrographs. Compared to X-ray computed tomography and destructive sectioning, the proposed approach overcomes the low material contrast between pipe and fitting, avoids long scan times, and enables a nondestructive, potentially inline-capable quantitative assessment of sub-millimeter grooves. A manual high-frequency ultrasound evaluation with a single probe and conceivable rule-based time-of-flight pipelines with hand-crafted echo picking and thresholds both show only moderate agreement with CT references and require substantial feature engineering for multiple echoes. In contrast, the PAUT-CNN method exploits the full raw B-scan without explicit feature design and achieves a root mean square error of about 7% of the groove filling levels on a held-out test set, corresponding to an absolute error on the order of a few tens of micrometers in groove height. This demonstrates that high-frequency phased array ultrasound combined with data-driven evaluation can quantitatively assess the filling of sub-millimeter grooves in aerospace-relevant press-fit connections.