Antonio Zippo

In this study, an active vibration control (AVC) strategy is effectively used on a system made of a honeycomb polymer–paper core and carbon fiber-reinforced polymer (CFRP) plates. A cost-effective and practical solution based on an AVC system has been developed and tested using a microcontroller unit (MCU) from Texas Instruments. The control system is studied by applying out-of-plane disturbances to the composite panel via an electrodynamic shaker, by exciting the identified mode shapes obtained through experimental modal analysis, i.e., impact tests. The actuator chosen for the AVC system is a Macro Fiber Composite (MFC) patch. Multiple analog signal processing circuits were developed to scale and shift the signal at the input and output of the MCU. The proposed control algorithm is based on a positive position feedback (PPF) technique. Modal analysis was performed to identify the natural frequencies and mode shapes of the structure, which are essential for the design and tuning of the modal-based PPF controller. This analysis also enabled optimal sensor and actuator placement, ensuring effective targeting and control of the dominant vibration modes. Then, a series of tests were performed using pure sine excitations at frequencies of interest, close to the 2nd and 8th mode at 25.13 Hz and 129 Hz, respectively. The results of the experiments revealed a velocity attenuation of 55.8% to 76.9% and a Power Spectral Density (PSD) attenuation of 5.8 dB to 12.8 dB, depending on the mode under study. Owing to the size and mass properties of the Macro Fiber Composite (MFC) patches, the control system is very much suitable for automobile and aerospace applications.

The vehicle suspensions have the primary task of attenuating the forces coming from the road surface. The performance is directly linked to the stiffness of the suspension system. Traditional suspensions, composed of linear elements, effectively damp high frequencies but perform poorly at low frequencies. In this regard, non-linear suspensions, characterized by a non-linear force–displacement relationship, have been introduced. These types of suspensions achieve this characteristic by combining elements with positive stiffness with elements with negative stiffness, resulting in an equivalent system with quasi-zero stiffness (QZS) around the equilibrium. The performance of the QZS suspension system is analyzed here using the Multibody Dynamics software MSC Adams^® (2022.2). Static characteristics, transmissibility, and isolation performance are investigated through dynamic tests based on road profiles according to ISO 8608 regulations generated using MATLAB^® (R2022b). The proposed quasi-zero stiffness suspension demonstrates an improvement of approximately 19% in vibration attenuation compared to a conventional suspension system under realistic road excitations.

Nonlinear vibration phenomena play a central role in modern engineering, spanning applications from large-scale civil infrastructure to microscale and nanoscale systems

Digital Twins (DTs) have become essential tools for the design, diagnostics, and prognostics of mechanical systems. In gearbox applications, DTs are often built using physics-based simulations guided by ISO standards. However, standards-based approaches may suffer from complexity, licensing limitations, and computational costs. The concept of symmetry is inherent in gear mechanisms, both in geometry and in operational conditions, yet practical applications often face asymmetric load distributions, misalignments, and asymmetric and symmetric nonlinear behaviors. In this study, we propose a hybrid method that integrates data-driven modeling with standard-based simulation to develop efficient and accurate digital twins for gear transmission systems. A digital twin of a spur gear transmission is generated using KISSsoft^®, employing ISO standards to compute safety factors across varied geometries and load conditions. An automated MATLAB-KISSsoft^® (COM-interface) enables large-scale data generation by systematically varying key input parameters such as torque, pinion speed, and center distance. This dataset is then used to train a neural network (NN) capable of predicting safety factors, with hyperparameter optimization improving the model’s predictive accuracy. Among the tested NN architectures, the model with a single hidden layer yielded the best performance, achieving maximum prediction errors below 0.01 for root and flank safety factors. More complex failure modes such as scuffing and micropitting exhibited higher maximum errors of 0.0833 and 0.0596, respectively, indicating areas for potential model refinement. Comparative analysis shows strong agreement between the NN outputs and KISSsoft^® results, especially for root and flank safety factors. Performance is further validated through sensitivity analyses across seven cases, confirming the NN’s reliability as a surrogate model. This approach reduces simulation time while preserving accuracy, demonstrating the potential of neural networks to support real-time condition monitoring and predictive maintenance in gearbox systems.