Iranian Society of Acoustics and Vibration and AvecinaJournal of Theoretical and Applied Vibration and Acoustics2423-47618220221111Detection of malfunction in ignition system for an internal combustion engine via artificial intelligence model12071534310.22064/tava.2024.555697.1206ENMohamadGohariAssistant Professor, School of Mechanical Engineering, Arak University of Technology, Arak, IRAN0000-0001-6744-2151AbbasPakAssistant Professor, Department of Engineering, Bu-Ali Sina University, Hamedan, IRAN0000-0002-4099-982XMasoudKazemiM.Sc. Student, School of Mechanical Engineering, Arak University of Technology, Arak, IRANJournal Article20220612Engine failure is a significant issue for drivers, often requiring substantial experience to identify and troubleshoot effectively. Repairing the engine based on probable causes and uncertainties can be time-consuming and costly. Recently, AI models, particularly those based on Artificial Neural Networks (ANN), have been developed and gained popularity in fault diagnosis. This paper considers two common faults in internal combustion engines - cylinder misfire and complete cylinder failure - caused by ignition system issues. An Artificial Neural Network fed by Statistical features (SANN) is employed to distinguish these two faults. The SANN was trained on statistical features derived from vibration signals and achieved an accuracy of over 90%. Thus, SANN can classify the fault generated by the ignition system. This model was further validated using a different engine as a second case, demonstrating its ability to predict fault types with acceptable accuracy. In fact, the SANN could find a malfunction of the engine mounted on a car perfectly. This capability enables operators to accurately identify the type of fault, allowing for more precise and efficient repairs. Therefore, the proposed method is well-suited for troubleshooting ignition system malfunctions and diagnosing related issues via a reliable fault detection modelhttps://tava.isav.ir/article_715343_89a470cd5724eab9a0d0c526677a1f60.pdfIranian Society of Acoustics and Vibration and AvecinaJournal of Theoretical and Applied Vibration and Acoustics2423-47618220221229Acoustical analysis of a structure with an auxetic honeycomb and internal resonator213770728610.22064/tava.2023.559832.1209ENMostafaKhosroupour ArabiM.Sc. Student, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran0000-0002-5606-0956RoohollahTalebitootiProfessor, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, IranJournal Article20220807Manmade metamaterials are used due to their different properties originating from their geometry. Sound attenuation is a property that depends on structure geometry. Small structures could reduce sound propagation within a mid-high frequency. To change the range of sound propagated in metamaterial from high-frequency to low-frequency, however, internal resonators could be used due to their rotational vibration having high effect on sound attenuation. In this paper, an acoustical analysis is done on a hexachiral lattice structure with an internal resonator capable of decreasing sound wave propagation among the structure in the low-frequency range, which causes corresponding bandgaps in this frequency range (1-4500 Hz). Geometry parameters that can affect the width and range of bandgaps are studied, including the radius of the resonator, the thickness of the ligament, and the distance between each node. It was found that the radius of the resonator has a positive impact on the ability of attenuation, but the distance between each node has a more negative impact on the bandgap. Optimization is done on the geometric parameters considering the weight of the structure, which leads to the construction of light structures capable of reducing sound propagation.