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 Points72876110.22064/tava.2025.2061787.1265ENMohammad NaeimMoradiMechanical Engineering Department, Amirkabir University of Technology (Tehran Polytech-nic), Tehran, Iran0000-0003-3133-994XMaryamGhasabzadehVehicle Technology Research Institute, Amirkabir University of Technology (Tehran Polytech-nic), Tehran, IranAbdolrezaOhadi, Mechanical Engineering Department, Amirkabir University of Technology (Tehran Poly-technic), Tehran, Iran0000-0001-6514-4089Journal Article20250526This 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_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.pdf