The damage that occurs around the tire bead region is one of the critical failure forms of a tire. Generally, the prediction of tire durability is carried out by the experimental method. However, it takes a lot of money and time to conduct experiments. Therefore, to determine the fatigue life of radial tire bead, a reasonable prediction method is proposed in this paper. Fatigue testing of bead rubber compounds to determine the ΔSED-number of the cycle (N_f) was applied. The maximum strain energy density range (ΔSED_max) of several bead compounds was obtained by steady-state rolling analysis with a finite element method. This quantity is then inserted into a fatigue life equation to estimate the fatigue life. The experimental results of the 175/75R14 tire were compared with the estimated value, which showed a good correlation. It is concluded that the method can be effectively applied to the fatigue life prediction of the tire bead.

Vehicle tires are major components that are subjected to fatigue loading and their durability is of economic interest as it is directly related to the safety of property and the life of producers and consumers. Tire durability is also a major issue of energy conservation and environmental protection. This research aims to establish a reasonable fatigue evaluation and optimization method that effectively improves tire fatigue life. In the study, 11.00R20 and 12.00R20 all‐steel radial truck tires were the research objects, and the guiding hypothesis for the research was that “the maximum area of ​​the strain energy density gradient modulus corresponds to the initial failure area, its direction corresponds to the crack propagation direction, and also the maximum strain energy value is inversely proportional to the tire fatigue life.” Through finite element analysis and durability test, the strain energy density gradient was determined as tire fatigue evaluation index, and the hypothesis of tire fatigue life prediction was validated. At the same time, the sensitivities of strain energy gradient to the tire structure parameters were calculated. Besides, the relationship between the structure parameters and the fatigue life was as well established in this paper. This study has formulated a tire fatigue evaluation method and proposed an effective optimization method for enhancing tire fatigue life. The results obtained are of high application value in offering guidance for tire structural design and useful for refining the fatigue failure theory of truck radial tires and improving durability.

The wet grip performance of tire is one of the important performances affecting vehicle safety. The steering, acceleration, and braking of the vehicle are directly affected by the grounding characteristics between the radial tire and the ground. In order to study the influence of grounding characteristics of the tire on wet grip performance, ten 205/55R16 tires produced by different manufacturers were selected and tested. The grounding characteristics of the tires were tested using an optical test rig for tire grounding pressure distribution, considering inflation pressure distribution, load and wheel alignment. The tire-road contact area was subdivided into five parts, and 69 parameters were used to describe the grounding characteristics. A software was proposed to process the test results automatically, and 69 grounding characteristic parameters of each tire were obtained. Correlation analysis on tire wet grip performance and grounding characteristics was used for selecting the principal parameters. Finally, eight grounding characteristic parameters related to tire wet grip performance was obtained. Among them are five grounding characteristic parameters (central area rectangle ratio, central area width, internal shoulder length-to-width ratio, external and internal shoulder contact area ratio, external and internal shoulder impression area ratio) which have high correlation to tire wet grip performance, and three grounding characteristic parameters (external shoulder width, external shoulder length-to-width ratio, external and internal shoulder width ratio) which have low correlation to the wet grip performance of the tire. The principal component analysis method was used to analyze the highly correlated grounding characteristic parameters, and the regression equation for evaluating tire wet grip performance was fitted. The comparison of experimental and fitted values show that the errors are within 4%. The result demonstrates that, the method for evaluating wet grip performance of the radial tire through tire-road grounding characteristics was achieved.

An analytical model of a non-pneumatic tire is proposed to study the static deformation responses of a non-pneumatic tire in contact with a rigid ground. The tire consists of a shear band which is formed by an annular beam, and elastic spokes that connect the shear band to the rigid hub of the tire. The shear band is modeled using a Timoshenko beam. The spokes are modeled by linear springs, which are distributed evenly in circumferential direction. Governing equations of the model were derived using a theoretical analysis. The shear band static deformation was obtained based on the discussion of the relationship between spoke stiffness and the parameters of the shear band. A finite element model was developed to verify the accuracy of the model. As a part of the results from this study, a parametric analysis of quantities of interest for the tire is presented, which can be used in improving the optimal design of non-pneumatic tires. This scheme offers a holonomic solution for the complicated differential equations and gives a computationally efficient tool for rapid analyzing and designing such systems.

Vehicle traffic noise has become an important factor in urban noise pollution. With the increase in the number of new-energy vehicles, the current situation of tire/road noise as one of the main noise sources of automobiles is becoming increasingly prominent. Tire noise prediction is the basis for optimizing the design of low-noise tires, providing a reference basis for tire design with low-noise performance. This paper used deep learning methods to predict the noise performance of TBR radial tires. We obtained descriptive statistical features of tire structure and tread pattern images as input to the model, and used measured tire/road noise as output, then we constructed a multi-modal tire noise dataset. Comparing the prediction performance of three pre-trained transfer models such as Resnet18, VGG16, and Inception V3 on the tire pattern images, the Resnet18 had the best prediction effect. Resnet18 was selected as a feature extractor to extract image features, which fuse with the tire structural features at the feature level. The experiment constructed the TLMF-TRNP model to predict tire noise. The experimental results of the TLMF-TRNP model indicated that RMSE, MAE, and R ² were 0.1337, 0.0948, and 0.9864 respectively, achieving ideal prediction accuracy on a small-scale tire noise dataset and controlling the absolute error of the test tires within ±0.4 dB effectively. An accurate tire noise prediction model will provide a theoretical basis for tire design with low noise tires.