The Assembly Process Optimization Of The Third-generation Wheel Hub Bearing Units Based On Date-driven

With the higher requirement about the performance of the automotive,and the promotion and application of the green manufacturing technology worlwide,how to reduce the energy consumption in the assembly process of the third-generation wheel hub bearing units,has become the direction for engineer to explore.Combining with the finite element analysis method and data-driven technology,an optimization design method for the assembly process of the third-generation wheel hub bearing units based on data-driven is proposed,through the effective finite element model,it obtains the performance parameters of hub bearing under different process parameters,and uses the adaptive constraint optimization algorithm based on data-driven to achieve the optimization design of the assembly process of the third-generation wheel hub bearing.Under the premise of ensuring the comprehensive performance of the automotive hub bearing,it minimizes the energy consumed in the entire assembly process and realizes the concept of green manufacturing.Based on the above research ideas,the content of this study specifically includes:(1)Determining the design variables,objective function and constraint function of the assembly process optimization model of the third-generation wheel hub bearing units,and a method is proposed for the assembly process optimization of the third-generation wheel hub bearing units based on data-driven.(2)According to the material tensile test,the roughing test of the circular pier and the calculation,it obtains the material parameters,friction coefficient and space trajectory of the rivet head and et al,and completes the finite element analysis of the press-fitting process and the shaft clinching process.The ALE adaptive grid strategy and the appropriate mass amplification factor is used to improve the computational efficiency of the model,and the finite element model is verified effective through comparative analysis of the experimental results and simulation results.Which can provide valid data for the the assembly process optimization of the third-generation wheel hub bearing units.(3)Applying multiple surrogate model technology,an adaptive constraint optimization algorithm based on data-driven is proposed.Surrogate model with higher fitting accuracy is selected for the objective function and constraint function of the constraint optimization problem,and an approximate model of the original problem is constructed.The sequential quadratic programming algorithm is applied to implement the optimization calculation,and the initial value required by the sequential quadratic programming algorithm is determined by the stochastic ranking method.The sample space is obtained by sequential sampling method.and MATLAB is used to complete the programing of the algorithm.The key parameter k of the algorithm is determined by two classic mathematical optimization models,and apply the two models to verify the validity,accuracy and robustness of the algorithm.(4)Based on the effective finite element model,get the range of design variables and determine the final assembly process optimization model of the third-generation wheel hub bearing units.The optimal design of the assembly process of the third-generation wheel hub bearing units is realized by adaptive constraint optimization algorithm based on data-driven.The results show that the optimized forming surface and initial position of the rivet head,on the basis of guaranteeing the comprehensive performance of the third-generation hub bearing units,and the energy consumption in the entire assembly process is reduced by 23.73%.