Search Results (2)

Traditional pneumatic tyres are prone to puncture or blowout and other safety hazards. Non-pneumatic tyres use a high-strength, high-toughness support structure to replace the “airbag body” structure of pneumatic tyres, which is made of fibre skeleton materials and rubber laminated layers, thus effectively avoiding the problems of blowout and air leakage. However, discontinuous spokes undergo repeated bending deformation when carrying loads, which leads to energy loss, of which the rolling resistance of non-pneumatic tyres is one of the main sources of energy loss. This paper focuses on the study of gradient honeycomb non-pneumatic tyres. Firstly, a finite element model was established, and the accuracy of the model was verified by numerical simulation and stiffness tests. Secondly, the order of the effect of different spoke thicknesses on rolling resistance was obtained through orthogonal test analysis of four-layer honeycomb spoke thicknesses. Then, four optimized design variables were selected in combination with the spoke angles, and the effects of the design variables on rolling resistance were analyzed in detail by means of the Latin hypercube experimental design. Finally, the response surface model was established, and the non-linear optimization model was solved by the EVOL optimization algorithm considering the tyre stiffness limitations so that the rolling resistance was minimized. The results of the study laid down theoretical and methodological guidance for the design concept and technological innovation of low rolling resistance comfort non-pneumatic tyres. Full article

Tyre pressure monitoring systems (TPMS) are electronic devices that monitor tyre pressure in vehicles. Existing systems rely on wheel speed sensors or pressure sensors. They rely on batteries and radio transmitters, which add to the expense and complexity. There are two types of basic tyres: non-pneumatic and pneumatic tyres. Non-pneumatic tyres lack air and combine the tyre and wheel into a single unit. When it comes to noise reduction, durability, and shock absorption, pneumatic tyres are more valuable than non-pneumatic tyres. In this study, nitrogen-filled pneumatic tyres were considered due to the uniform pressure management property. Additionally, nitrogen has less of an effect on thermal expansion than regular air-filled tyres. This work aimed to offer a deep learning approach for TPMS. An accelerometer captured vertical vibrations from a moving vehicle’s wheel hub, which were then converted in the form of vibration plots and categorized using pretrained networks. The most popular pretrained networks such as AlexNet, GoogLeNet, ResNet-50 and VGG-16 were employed in this study. From these pretrained networks, the best-performing pretrained network was determined and suggested for TPMS by varying the hyperparameters such as learning rate (LR), batch size (BS), train-test split ratio (TR), and solver (SR). Findings: A higher classification accuracy of 97.20% was obtained while using ResNet-50. Full article