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The properties of tires related to their viscoelastic behavior have a significant impact in the field of vehicle dynamics. They affect the performance and safety of a vehicle based on how they change when the tire performs in variable thermal conditions, interacts with various kinds of road surfaces, and accumulates mileage over time. To analyze and understand such properties of viscoelastic materials, destructive tests like dynamic mechanical analysis (DMA) are used, which make the tire unusable after the test; these are usually carried out on specimens cut from the zone of interest. The development of an innovative testing methodology connected to a hardware device called VESevo allows the characterization of the viscoelastic properties of tire compounds belonging to tread or other parts in a fast and nondestructive way. This new device provides valuable information about the evolution of the tire’s viscoelastic properties, allowing it to monitor them throughout the whole lifecycle. In the paper, an overview of the possible sensitivities that can be investigated thanks to the VESevo is provided: The tread viscoelasticity was characterized and monitored for several tire tread compounds, over tire mileage, over tread thermal curing cycles, and as an index of the tread quality and uniformity in production. Preliminary results were collected and are presented. In the final paragraph, further recent applications developed from the tire field, which are not directly related, are reported.

Polymers find widespread applications in various industries, such as civil engineering, aerospace, and industrial machinery, contributing to vibration control, dampening, and insulation. To accurately design products that are able to predict their dynamic behavior in the virtual environment, it is essential to understand and reproduce their viscoelastic properties via material physical modeling. While Dynamic Mechanical Analysis (DMA) has traditionally been used, innovative non-destructive techniques are emerging for characterizing components and monitoring their performance without deconstructing them. In this context, the Time–Temperature Superposition Principle (TTSP) represents a powerful empirical procedure to extend a polymer’s viscoelastic behavior across a wider frequency range. This study focuses on replicating an indentation test on viscoelastic materials using the non-destructive Viscoelasticity Evaluation System evolved (VESevo) tool. The primary objective is to derive a unique temperature–frequency relationship, referred to as a “shift law”, using characteristic curves from this non-invasive approach. Encouragingly, modifying the device setup enabled us to replicate, virtually, three tests under identical initial conditions but with varying indentation frequencies. This highlights the tool’s ability to conduct material testing across a range of frequencies. These findings set the stage for our upcoming experiment campaign, aiming to create an innovative shift algorithm from at least three distinct master curves at specific frequencies, offering a significant breakthrough in non-destructive polymer characterization with broad industrial potential.

Since the beginning of their production, pneumatic tires have experienced tremendous improvements in structure and materials, becoming the dominant design in the world tires market. Nevertheless, relying upon pressurized air, they are affected by maintenance and security issues that can lead to fatal accidents. Therefore, tire-makers are investigating new tire designs, called Airless or Non-Pneumatic, with the aim of removing air-related problems. The research about such tires is still at an early stage, especially if compared to the one conducted on the pneumatic ones. In this paper, the development of a methodology capable of studying the mechanical behavior of a Non-Pneumatic Tire (NPT) by means of experimental data and numerical approach is illustrated. The experimental activities consisted of a scanner acquisition of the NPT and a footprint analysis for the calculation of the radial stiffness and contact patch pressure distribution. Moreover, the Digital Image Correlation (DIC) technique was applied to carry out a more specific study about the spoke’s deformation. From the acquired 3D model, a calculation of the NPT vertical deflection with finite element analysis (FEA) was performed—validating the model and then submitting it to a steady state analysis—that allows the simulation of a steady state rolling tire with the possibility to replicate different values of slip ratio. The results of the experimental activities are in good agreement with the ones obtained with FEA, further validating the developed methodology.

While in the automotive field the relationship between road adherence and tire temperature is mainly investigated with the aim to enhance the vehicle performance in motorsport, the motorcycle sector is highly sensitive to such theme also from less extreme applications. The small extension of the footprint, along with the need to guarantee driver stability and safety in the widest possible range of riding conditions, requires that tires work as most as possible at a temperature able to let the viscoelastic compounds-constituting the tread and the composite materials of the whole carcass structure-provide the highest interaction force with road. Moreover, both for tire manufacturing companies and for single track vehicles designers and racing teams, a deep knowledge of the thermodynamic phenomena involved at the ground level is a key factor for the development of optimal solutions and setup. This paper proposes a physical model based on the application of the Fourier thermodynamic equations to a three-dimensional domain, accounting for all the sources of heating like friction power at the road interface and the cyclic generation of heat because of rolling and to asphalt indentation, and for the cooling effects because of the air forced convection, to road conduction and to turbulences in the inflation chamber. The complex heat exchanges in the system are fully described and modeled, with particular reference to the management of contact patch position, correlated to camber angle and requiring the adoption of an innovative multi-ribbed and multi-layered tire structure. The completely physical approach induces the need of a proper parameterization of the model, whose main stages are described, both from the experimental and identification points of view, with particular reference to non-destructive procedures for thermal parameters definition. One of the most peculiar and challenging features of the model is linked with its topological and analytical structure, allowing to run in real-time, usefully for the application in co-simulation vehicle dynamics platforms, for performance prediction and setup optimization applications.

In the last few decades, the role of vehicle dynamics control systems has become crucial. In this complex scenario, the correct real-time estimation of the vehicle’s sideslip angle is decisive. Indeed, this quantity is deeply linked to several aspects, such as traction and stability optimization, and its correct understanding leads to the possibility of reaching greater road safety, increased efficiency, and a better driving experience for both autonomous and human-controlled vehicles. This paper aims to estimate accurately the sideslip angle of the vehicle using different neural network configurations. Then, the proposed approach involves using two separate neural networks in a dual-network architecture. The first network is dedicated to estimating the longitudinal velocity, while the second network predicts the sideslip angle and takes the longitudinal velocity estimate from the first network as input. This enables the creation of a virtual sensor to replace the real one. To obtain a reliable training dataset, several test sessions were conducted on different tracks with various layouts and characteristics, using the same reference instrumented vehicle. Starting from the acquired channels, such as lateral and longitudinal acceleration, steering angle, yaw rate, and angular wheel speeds, it has been possible to estimate the sideslip angle through different neural network architectures and training strategies. The goodness of the approach was assessed by comparing the estimations with the measurements obtained from an optical sensor able to provide accurate values of the target variable. The obtained results show a robust alignment with the reference values in a great number of tested conditions. This confirms that the adoption of artificial neural networks represents a reliable strategy to develop real-time virtual sensors for onboard solutions, expanding the information available for controls.

One of the most important requirements of the Formula SAE (Society of Automotive Engineers) race car design competition is that the car structure must guarantee a high protection level in case of frontal impacts, by preventing intrusions into the driver foot zone or dangerous deceleration levels. These functions are mainly performed by the impact attenuators, which have to guarantee a high specific energy absorption capacity (SEA) and in order to preserve vehicle performances, have to be as light weighted as possible. The aim of this study is the design of an impact attenuator, to be mounted on a Formula SAE car, which main purpose is to obtain the optimal crash energy management, maximizing the absorbed energy and optimizing the geometry. The outcome of the study highlights that the absorber structure made up of honeycomb sandwich panels (primary energy absorbers), realized by means of different aluminium alloys employing an innovative design considering particular geometrical cavities within the structure, could lead to reduce the overall weight and to achieve a more progressive deformation during the impact.

One of the main challenges in maximizing vehicle performance is to predict and optimize tire behavior in different working conditions, such as temperature, friction, and wear. Starting from several approaches to develop tire grip and wear models, based on physical principles, experimental data, or statistical methods available in the literature, this work aims to propose a novel tire wear model that combines physical and statistical analysis on a large number of high-performance vehicle telemetries, tracks, and road data, as well as tires’ viscoelastic properties. Another contribution of this multidisciplinary study is the definition of the functional relationships that govern the tire–road interaction in terms of friction and degradation, conducting a thorough analysis of the car’s telemetry, the track and asphalt features, and the viscoelastic properties of the tires.

In the world of motorsports engineering, improving brake performance is a crucial goal. One significant factor that affects this performance is the increase in brake disc temperature due to reduced cooling airflow, a phenomenon called “blanking”. This temperature increase also impacts the rim and the air inside the tire, causing changes in tire temperature and pressure, which affects the vehicle’s performance. Properly adjusting the brake blanking can be essential to keep the tire running at the right temperature, resulting in maximization of the performance on track. To address this complex problem, this study describes the problem of cooling brake discs, and this problem is then used as an opportunity to introduce a new variable in order to optimize the performance of the vehicle. By changing the thermal evolution of the brake disc, through the blanking, it can change a large percentage of heat that heats the tire. When combining an existing brake model in the literature with a tire thermal model in a co-platform simulation, it was seen that it is possible to work these two models together with the aim of being able to obtain the prediction of the optimal blanking value to be adopted before proceeding on track, thus saving time and costs.

Appropriate modelling of the real behavior of viscoelastic materials is of fundamental importance for correct studies and analyses of structures and components where such materials are employed. In this paper, the potential to employ a generalized Maxwell model and the relative fraction derivative model is studied with the aim to reproduce the experimental behavior of viscoelastic materials. For both models, the advantage of using the pole-zero formulation is demonstrated and a specifically constrained identification procedure to obtain the optimum parameters set is illustrated. Particular emphasis is given on the ability of the models to adequately fit the experimental data with a minimum number of parameters, addressing the possible computational issues. The question arises about the minimum number of experimental data necessary to estimate the material behavior in a wide frequency range, demonstrating that accurate results can be obtained by knowing only the data of the upper and low frequency plateaus plus the ones at the loss tangent peak.

This paper presents an investigation on the influence of the design parameters in an underactuated mechanical finger driven by un-extendable tendons. The study was carried out using simulations and experimental tests. The aim of the study is to analyze the behavior of the finger during its closing motion. Hence, this study can help in correctly designing fingers for underactuated grasping devices. Various design aspects and parameters were taken into account to optimize the dynamic behavior of the mechanism in the simulation. The actions of the tendons were modelled with the forces that the tendon exerts on the phalanges.