https://tava.isav.ir/ Journal of Theoretical and Applied Vibration and Acoustics en daily 1 Thu, 01 Jan 2026 00:00:00 +0330 Thu, 01 Jan 2026 00:00:00 +0330 https://tava.isav.ir/article_728761.html This paper presents the design and simulation of acoustic metamaterial lenses that focus elastic waves at pre-determined focal points. The modified Luneburg refractive index profile is used in the design process to de-fine the focal point locations, which is a capability not previously explored in elastic wave research. This new approach is important because it enables more precise spatial control of waves, resulting in enhanced resolution for elastic wave focusing applications. Three lenses, each targeting specific focal points, are designed by pro-posing hexagonal unit cells containing blind holes with varying diameters. Dispersion curves are calculated by finite element simulations to determine wave properties of unit cells, including refractive indices. These unit cells provide a wide range of refractive indices (1.0314-1.4959) at the design frequency of 50 kHz which is suitable for constructing Luneburg lenses. Unit cells are then arranged according the discretized refractive index profiles to form the lenses. Numerical simulations validate effective wave focusing at the intended focal points (F=R, 1.5R, 2R) with three lenses. The highest amplification of waves and narrowest focal zone is for the lens with F=R. As focal point shifts toward 2R, wave distribution becomes scattered along the focal axis. Decay length analysis of F=1.5R and 2R lenses indicates their suitability for long distribution of high-velocity regions. Frequency-dependent simulations across 46–52 kHz reveal all lenses maintain efficient focusing be-tween 49–51 kHz. At more distant off-design frequencies, amplifications result from refractive index shifts that misalign the focal point. https://tava.isav.ir/article_732816.html With the rapid and continuous advancement of industry and considering the vital role of gearboxes in various machines and industrial systems, condition monitoring and maintenance of these systems are of great importance. One of the most common faults in industrial gearboxes is the occurrence of misalignment between meshing gears. The presence of misalignment provides favorable conditions for the development of gear and bearing defects. Given the widespread use of helical gears in most industrial gearboxes, a deeper investigation of their behavior under misalignment conditions is required. In this study, to bring simulations closer to real-world and industrial cases, experiments are conducted on an industrial gearbox operating under gear misalignment. A dedicated mechanism has been designed and fabricated to impose controlled misalignment. After data acquisition and extraction of vibration signals, a total of nine features is calculated and analyzed. The results reveal that among the extracted features, Energy Ratio and Kurtosis exhibited the highest percentage variations relative to the aligned condition. Furthermore, detailed analysis shows that these features demonstrated more significant increases in the horizontal direction and at measurement points closer to the meshing location of the misaligned gears compared to the aligned state. https://tava.isav.ir/article_733028.html Engine failure is a critical issue for drivers and often requires substantial experience to diagnose and resolve effectively. Attempting repairs based on guesswork or uncertain causes can lead to significant time loss and high costs. Recently, Artificial Intelligence (AI) models, particularly those based on Artificial Neural Networks (ANNs), have shown promising performance in fault diagnosis. This study focuses on detecting two common faults in internal combustion engines—cylinder misfire and complete cylinder failure—both typically caused by problems in the ignition system. A model referred to as WANN (Wavelet+ Artificial Neural Network) is proposed, which uses coefficients are derived from vibration signals by wavelet transformation. The WANN achieved over 90% classification accuracy in identifying ignition-related faults. To evaluate the model's generalizability, the WANN is also tested on a different engine, successfully classifying fault types with acceptable accuracy. Notably, the model accurately detected ignition faults in a vehicle-mounted engine, demonstrating its robustness and practical utility.This capability allows mechanics and technicians to accurately pinpoint the fault type, leading to more efficient and cost-effective repairs. Therefore, the proposed method offers a reliable and intelligent solution for diagnosing ignition system faults in automotive applications. https://tava.isav.ir/article_734015.html This paper examines the mechanical performance and optimization of the Ultrasonic-Assisted Simple Shear Extrusion (USSE) process. Conventional severe plastic deformation (SPD) methods often suffer from high friction, elevated forming forces, and microstructural non-uniformity. However, the USSE method addresses these limitations by applying high-frequency ultrasonic vibrations. In this study, Finite Element Analysis (FEA) and the Response Surface Method (RSM) are used together to model the process and identify optimal operating conditions. Three main input parameters, punch speed, resonant frequency, and vibration amplitude, are evaluated for their influence on forming force and plastic strain. The findings indicate that vibration amplitude is the dominant factor, contributing 90.89% to forming force reduction and 82.41% to plastic strain enhancement. Increasing vibration amplitude and lowering punch speed effectively decrease forming force while promoting higher plastic strain. RSM optimization suggested the optimal conditions as a vibration amplitude of 30.11 µm, a punch speed of 0.61 mm/min, and a resonant frequency of 21.68 kHz. Under these conditions, the USSE process significantly reduced forming force and substantially increased plastic strain compared to the conventional SSE method. Surface roughness measurements showed that Specimen P4 exhibited 21%, 9%, and 6% lower roughness than P1, P2, and P3, respectively. Additionally, the optimized USSE sample demonstrated a 10% improvement in ultimate tensile strength and an 82% reduction in grain size relative to the SSE specimen. These outcomes confirm the effectiveness of the USSE technique and its superior mechanical and microstructural advantages. https://tava.isav.ir/article_734183.html Rolling element bearings (REBs) are critical components in rotating machinery, where reliable operation depends on accurate and timely fault diagnosis. This paper introduces a deep transfer learning framework designed to achieve robust cross-domain fault diagnosis across both laboratory and industrial environments. The framework integrates a Stacked Autoencoder (SAE) for hierarchical feature extraction with a Deep Neural Network (DNN) classifier, while leveraging Adaptive Batch Normalization (AdaBN) and selective fine-tuning of the output layer to effectively address domain shifts. The main contribution lies in the combined use of SAE-based feature learning, AdaBN-driven distribution alignment, and limited-sample fine-tuning using small sets of labeled industrial and fixed-condition laboratory data, enabling high diagnostic reliability under diverse operating conditions. To assess the contribution of each component, a baseline version of the model without the fine-tuning stage was also evaluated. The substantial performance degradation observed when testing on unseen target domains confirms the essential role of fine-tuning for achieving robust generalization. The proposed method was validated using laboratory datasets collected under variable and fixed operating conditions, as well as an industrial dataset consisting of four states, including healthy (H), inner race fault (IRF), outer race fault (ORF), and rolling element fault (REF). Experimental results show that the complete framework provides stable and accurate fault classification, achieving 92.65% accuracy on industrial data and 90.91% on fixed-condition laboratory data, consistently outperforming the baseline and conventional deep learning approaches in cross-domain scenarios. https://tava.isav.ir/article_734184.html Vibration sensors are key equipment in monitoring the condition and performance of rotating systems, especially in sensitive industries such as oil and gas and power plants. In rotating systems with high moment of inertia, such as large steam and gas turbines, due to the low frequency range of vibrations, the use of speed transducers with appropriate sensitivity is essential. This study is dedicated to the design, simulation, and construction of a single-coil vibration sensor, which was developed with the aim of achieving a sensitivity of 10 mV/mm/s. The various components of the transducer, including the permanent magnet, coil, flat springs, and housing, were designed and fabricated using a reverse-engineering approach supported by detailed analysis of an existing commercial sensor. To determine the material and physical characteristics of the parts, experimental tests and field measurements were used, and modeling based on the law of electromagnetic induction was carried out to analyze the behavior of the sensor. Experimental results show that the produced sensor has an entirely linear voltage-velocity behavior with a sensitivity slope of 9.4374 mV/mm/s, which can be accurately compensated using a calibrated transmitter circuit. The operation of this sensor in the frequency range of 3 to 1200 Hz makes it a suitable option for use in heavy industrial conditions. https://tava.isav.ir/article_735807.html In the heavy vehicle transport industry, the fifth wheel coupling system is used as important component for connecting the semi-trailer with the tractor. Misfunction, corrosion or fracture of the fifth wheel can affect the stability and consequently the safety of the heavy vehicles. During operation, the fifth wheel experiences different loading conditions. So, a reliable model which represents stresses due to acting forces on the fifth wheel coupling system plays a significant role. For this purpose, first, controlled vibration experiments are conducted on a JOST’s fifth wheel coupling system and modal parameters of the system is obtained. Moreover, a finite element model (FEM) is constructed in ABAQUS software and successfully validated by the results of the experimental modal analysis (EMA). Then, the validated FEM is used to achieve the stress distribution in the system for accelerating and breaking conditions. Also, it is found that stresses in the JOST’s fifth wheel coupling system remain below the mechanical strength limit of the material. https://tava.isav.ir/article_736244.html This study performs a theoretical free vibration analysis of pre-twisted rectangular and airfoil-section blades using a global Ritz-based continuous classical beam–shaft reduced-order model (ROM), demonstrating its applicability in gas turbine design workflows. The framework incorporates pre-twist effects and static imbalance (CG–EA offset), which is horizontal in symmetric airfoils and also vertical in cambered or asymmetric airfoil sections. Validation is performed against finite element method (FEM) analyses using MSC Nastran and results from an advanced finite difference method (FDM) reported in the literature. The results show strong agreement with other works while maintaining computational efficiency. The proposed framework provides a basis or ROM-based tools capable of performing computationally efficient aeroelastic analyses of continuous blade models, blade rows, and integrated gas turbines. Torsional, flapwise, and chordwise bending modes—including their couplings induced by pre-twist and static imbalances—are examined, and natural frequency trends for different imbalance configurations are presented as functions of pre-twist angle