Considering the surface roughness of piston ring and cylinder liner and deformation of cylinder liner, the friction dynamics calculation model of cylinder liner-piston ring friction pair was established. The effects of top ring, other piston rings and cylinder liner surface roughness on friction performance were analyzed by grouping comparison and Box-Behnken response surface methodology. The calculation results show that when the surface roughness of the piston ring increases, the friction of the asperity increases slightly, the friction of the fluid decreases, and the total friction loss decreases. When the surface roughness of the cylinder liner increases, the friction of the asperity decreases slightly, the friction of the fluid increases, and the total friction loss increases.

With the rapid development of artificial neural networks, more sophisticated network models and more accurate prediction results are provided for solving engineering applications. In this paper, the weights and thresholds of the feedforward neural network model were optimized using the pelican algorithm, and the optimal solution was output by simulating the pelican predation scheme and assigned as the new parameters of the neural network. A POA-BP network model was proposed, and its better prediction was demonstrated by comparing the fitting and prediction performance with 13 neural network models such as random forest, support vector machine, and wavelet basis by evaluating metrics such as RMSE, MSE, and MAE. To further improve the prediction accuracy, different hidden layer topologies of POA-BP were compared, and the Monte Carlo method was used to obtain seven design variables for the lithium battery shell size parameters, and parameter regression prediction was performed for the structure after the variable density topology optimization used the isotropic material interpolation model (SIMP) with the moving asymptote method by invoking the MinGW-w64 compiler, and the 1-3-1 neural network was selected model to predict each dimension of the battery shell structure, the final shell weight reduction ratio was 18.12% and the first-order intrinsic frequency was increased by 14.56%.

The wheel hub is an important component of the wheel, and a good hub design can significantly improve vehicle handling, stability, and braking performance, ensuring safe driving. This article optimized the hub structure through morphological aspects, where reducing the hub weight contributed to enhanced fuel efficiency and overall vehicle performance. By referencing honeycombed structures, a bionic hub design is numerically simulated using finite element analysis and response surface optimization. The results showed that under the optimization of the response surface analytical model, the maximum stress of the optimized bionic hub was 109.34 MPa, compared to 119.77 MPa for the standard hub, representing an 8.7% reduction in maximum stress. The standard hub weighs 34.02 kg, while the optimized hub weight was reduced to 29.89 kg, a decrease of 12.13%. A fatigue analysis on the optimized hub indicated that at a stress of 109.34 MPa, the minimum load cycles were 4.217 × 10⁵ at the connection point with the half-shaft, meeting the fatigue life requirements for commercial vehicle hubs outlined in the national standard GB/T 5334-2021.

According to the honeycomb structure, the preliminary design of bionic wheel hub for finite element analysis, in the working condition of a wheel by the force is through the spokes pointing to the center, the working condition of two wheel by the force is through the two spokes pointing to the center, and the ordinary wheel stress and displacement value results compared, observed the maximum stress value appears in the connection between the center of the wheel and the spokes, and predict the fatigue life of the preliminary design of bionic wheel. Using finite element analysis and response surface optimization analysis method for numerical simulation, through two optimization results show that, in the use of response surface analysis model optimization, the optimized bionic wheel maximum stress is 109.34MPa, compared with the ordinary wheel maximum stress 119.77MPa, the maximum stress reduced by 8.7%. The mass of the ordinary wheel is 34.02Kg, using the response surface optimization method, the optimized wheel mass is 29.89Kg, the mass reduced by 12.13%. The optimized wheel fatigue analysis, in the stress of 109.34MPa, load loading times at least 4.217e+005, in the position at the junction with the half shaft. In line with the national standard GB/T 5334-2021[16] for commercial vehicle wheel fatigue life requirements.

The application of neural network model in engineering prediction is frequent. The BPE shell material was optimized, and the reliability of the new material was verified by modal simulation. The accuracy of finite element modeling was ensured by constrained mode experiments, and all variables were preprocessed by Latin hypercube sampling. The design parameters were determined by Monte Carlo simulation. Four different neural networks, including back propagation (BP), radial basis function (RBF), extreme learning machine (ELM) and wavelet neural network (WNN), are used to train and learn the dataset. The BPE weight reduction ratio was 14.3%, the stress was reduced by 18.6%, deformation displacement was reduced by 14.2%, and the first-order mode was increased by 29.1%.