Abstract: An analysis of geometric defects in plate assembly under linear-elastic conditions is investigated. A hybrid FEM–PINN (MDR) model is proposed, combining the finite element method, the analytical dimensionality reduction method, and physics-informed neural networks. This approach enables the coupling of physics-based modeling with data-driven learning. PINNs explicitly incorporate governing physical laws, including constitutive relations and contact conditions, into the training process using the dimensionality reduction method solutions, thereby ensuring the physical consistency of the predicted responses. This integration provides a trade-off between high-fidelity numerical modeling and reduced computational complexity. Additionally, the use of machine learning enhances computational efficiency while maintaining robustness, accuracy, and reliability in analyzing mechanical assemblies. The methodology is validated through a case study of a simple linear-elastic component assembly, and the results are given and discussed to demonstrate its effectiveness.

Key words: Flexible assembly, Fem, Physics neural networks, Dimensionality reduction.

Abstract : This paper investigates the bending vibration behavior of functionally graded material (FGM) beams with inherent uncertainties in material and geometric properties. The study employs the Stochastic Wave Finite Element (SWFE) method to quantify the influence of these uncertainties on wave propagation and dispersive characteristics. The material gradation is described explicitly through the volume fraction distribution, and the effective properties are derived accordingly. Based on the Euler–Bernoulli beam theory, the elemental mass and stiffness matrices are formulated and integrated into the WFE framework to extract the dispersion curves and wavenumbers. Uncertainties are modeled as Gaussian random variables and propagated through Monte Carlo simulations to compute the mean and standard deviation of the wavenumber. The obtained stochastic dispersion results highlight the sensitivity of bending wave propagation to variations in FGM parameters. The findings demonstrate that the SWFE approach efficiently captures the probabilistic behavior of composite structures and provides a reliable basis for analyzing and designing uncertain FGM systems

Key words: Uncertainty, Wave finite element method, Functionally graded material, dispersion.

Abstract: This paper presents a detailed numerical study of cavitating flow over a NACA66 hydrofoil at a cavitation number of 0.99. Validation against experimental data demonstrated accurate prediction of cavity dynamics, lift and drag forces, and shedding frequency. The effects of surface roughness were analyzed, showing performance degradation with increased roughness, where a smooth surface yielded the best hydrodynamic efficiency and stable cavitation behavior. The study underscores the potential of the surface texture as a passive control method to mitigate cavitation-induced instabilities while preserving hydrofoil performance, providing valuable insights for the design of efficient cavitating hydrofoils and propulsors.

Key words: CFD, FLUENT, cavitation, roughness, control.

Abstract: Despite the advent of new materials, carbon steel remains of paramount importance to the industrial world. Materials are the source of numerous defects during their processing, as well as during their use under the action of fatigue, corrosion, and accidents. Non-destructive testing is used to detect, in a part, without damaging it and while respecting its integrity, any particularity of its structure that could influence its behavior in service. Visual inspection is the simplest of all non-destructive testing techniques. External defects can be indicated by penetrating or magnetic methods. Internal defects are highlighted by ultrasound or radiography. The purpose of this work is to study the method of non-destructive testing by eddy currents, in order to evaluate its performance in the industry.

Key words: magnetism, eddy currents, welding, sensor, defects.