Iranian Society of Acoustics and Vibration and AvecinaJournal of Theoretical and Applied Vibration and Acoustics2423-476112120260101Design and simulation of acoustic metamaterial luneburg lenses for predetermined focal points11572876110.22064/tava.2025.2061787.1265ENMohammad NaeimMoradiPhD student, Acoustics Research Laboratory, Mechanical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, IRAN.0000-0003-3133-994XMaryamGhasabzadehAssistant Professor, Vehicle Technology Research Institute, Amirkabir University of Technology (Tehran Polytechnic), Tehran, IRAN.AbdolrezaOhadiProfessor, Acoustics Research Laboratory, Mechanical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, IRAN.0000-0001-6514-4089Journal Article20250526<span>This paper presents the design and simulation of acoustic metamaterial lenses that focus elastic waves at predetermined focal points. <span>The modified Luneburg refractive index profile is used in the design process to define focal point locations, a capability not previously explored in elastic wave research.</span><span> This new approach is important because it enables more precise spatial control of waves, resulting in enhanced resolution for elastic wave focusing applications.</span><span> </span>Three lenses, each targeting specific focal points, are designed by proposing 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 to 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 the narrowest focal zone are for the lens with F=R. As the 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 that all lenses maintain efficient focusing between 49–51 kHz. At more distant off-design frequencies, amplifications result from refractive index shifts that misalign the focal point.</span>https://tava.isav.ir/article_728761_254cc17c295b0b240b7ab68c79fd5829.pdfIranian Society of Acoustics and Vibration and AvecinaJournal of Theoretical and Applied Vibration and Acoustics2423-476112120260101Innovative Mechanism Design for Data Mining and Enhanced Gear Misalignment Detection via Vibration Analysis73281610.22064/tava.2025.2070794.1274ENAmirhosseinAmirniaMechanical Engineering Department, Sharif University of TechnologySomayeMohammadiAssistant Professor, Mechanical Engineering Department, Sharif University of Technology, Tehran, IRAN.0000-0003-3418-3987MehdiBehzadProfessor, Mechanical Engineering Department, Sharif University of Technology0000-0002-4042-6503Journal Article20250905With 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_732816_b1b32d9c47ad8b37c7388e1ae130e996.pdfIranian Society of Acoustics and Vibration and AvecinaJournal of Theoretical and Applied Vibration and Acoustics2423-476112120260101Application of Combined Wavelet Transformation and Neural Network in Electrical System Malfunction Detection of Engine73302810.22064/tava.2025.2057650.1264ENMohammadGohariAssociate Professor, School of Mechanical Engineering, Arak University of Technology, Arak, IRAN.0000-0001-6744-2151AbbasPakAbbas Pak, Faculty of Mechanical Engineering, Engineering School, Bu Ali Sina University, Iran0000-0002-4099-982XMasoudKazemiFaculty of Mechanical Engineering, Arak University of Technology, IranFarzadRafieianDepartment of Mechanical Engineering, Arak University of Technology, Arak, IRAN.0000-0003-1595-466XJournal Article20250411Engine 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.<br><br>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_733028_50c53928d280946955d7b8be8e243679.pdfIranian Society of Acoustics and Vibration and AvecinaJournal of Theoretical and Applied Vibration and Acoustics2423-476112120260101Investigation of Mechanical Properties and Optimization of Ultrasonic-Assisted Simple Shear Extrusion Process Using the Response Surface Method73401510.22064/tava.2026.2070911.1275ENMostafaBalaliMechanical Engineering Department, University of Birjand, Birjand, IranSeyed Mohammad HosseinSeyedkashiMechanical Engineering Department, University of Birjand, Birjand, Iran0000-0002-1544-0733AliHasanabadiMechanical Engineering Department, University of Birjand, Birjand, IranHamidGorjiMechanical Engineering Department, Babol Noshirvani University of Technology, Babol,
IranHamidBaseriMechanical Engineering Department, Babol Noshirvani University of Technology, Babol,
IranMohammadKhosraviMechanical and Materials Engineering Department, Birjand University of Technology, Birjand, IranJournal Article20250908This 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_734015_8922a964d74e4755624bcfb7ab606861.pdfIranian Society of Acoustics and Vibration and AvecinaJournal of Theoretical and Applied Vibration and Acoustics2423-476112120260101Enhancing Fault Diagnosis of Rolling Element Bearings: A Novel SAE-DNN Approach with AdaBN for Domain Adaptation73418310.22064/tava.2026.2070780.1273ENAliDavoodabadiDepartment of Mechanical Engineering, Sharif University of TechnologySomayeMohammadiMechanical Engineering Department, Sharif University of Technology0000-0003-3418-3987Hesam AddinArghandDepartment of Mechanical Engineering, Faculty of Engineering, University of Zanjan0000-0002-3191-3510MehdiBehzadProfessor, School of Mechanical Engineering, Sharif University of Technology, Tehran, IRAN.0000-0002-4042-6503Journal Article20250903Rolling 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_734183_d5ccad3b5dad2f8ec7e49c3fc1a0ce7f.pdfIranian Society of Acoustics and Vibration and AvecinaJournal of Theoretical and Applied Vibration and Acoustics2423-476112120260101Design and Fabrication of Low-Frequency Inductive Velocity Transducer for Condition Monitoring of Large-Scale Steam Turbines73418410.22064/tava.2026.2059962.1267ENAlirezaAlaviFaculty of Mechanical Engineering, University of Guilan0009-0007-0259-4606AliChaibakhshFaculty of Mechanical Engineering, University of Guilan0000-0001-9529-1784Journal Article20250703Vibration 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_734184_483dae31cec5d16e8a8e2654247ebd45.pdf