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Investigation of the Mileage Effects on the Viscoelastic Properties by a Non-destructive Method

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Advances in Italian Mechanism Science (IFToMM Italy 2022)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 122))

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Abstract

The viscoelastic properties of tires play a fundamental role into vehicle dynamics field affecting the vehicle performance and safety according to their evolution over the mileage. The knowledge of these properties is obtained through destructive tests, such as the Dynamic Material Analysis, which make the tire unusable. In this scenario, the Applied Mechanics research group of the Department of Industrial Engineering at the Federico II has developed an innovative device, called VESevo, capable of providing a smart and non-destructive characterization of the viscoelastic properties of tires tread compound. This new device allows to characterize tires obtained important information about the evolution of viscoelastic properties of a same tire over its mileage, opening scenarios of interest in a very broad panorama of applications ranging from the monitoring of the material performance during its whole lifecycle, to the quantitative analysis of products quality and repeatability of production processes. In this work, the authors show how the viscoelastic properties of a tire change as function of its mileage.

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Funding

This work was partly supported by the project “FASTire (Foam Airless Spoked Tire): Smart Airless Tyres for Extremely-Low Rolling Resistance and Superior Passengers Comfort” funded by the Italian MIUR “Progetti di Ricerca di Rilevante Interesse Nazionale (PRIN)”–grant no. 2017948FEN.

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Correspondence to Vincenzo Arricale .

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Timpone, F., Contrada, G., Sanfelice, M., Arricale, V., Napolitano Dell’Annunziata, G. (2022). Investigation of the Mileage Effects on the Viscoelastic Properties by a Non-destructive Method. In: Niola, V., Gasparetto, A., Quaglia, G., Carbone, G. (eds) Advances in Italian Mechanism Science. IFToMM Italy 2022. Mechanisms and Machine Science, vol 122. Springer, Cham. https://doi.org/10.1007/978-3-031-10776-4_50

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