基于径向基函数神经网络重载发动机曲轴的可靠性分析OA北大核心

In order to address the high computational cost and low accuracy issues of traditional reliability analysis methods,a reliability analysis method for heavy-duty engine crankshafts was proposed,combining grey relational analysis(GRA),radial basis function neural network(RBFNN)and particle swarm optimization(PSO).The dangerous working conditions of the crankshaft were identified based on dynamic analysis and firing work conditions,and a static calculation model was established using the finite element software ANSYS Workbench.The uncertainty factors affecting the maximum Mises stress were identified based on the crankshaft's geometric parameters and overall structure,and GRA was performed to determine the input parameters for RBFNN.Finally,optimal Latin hypercube sampling(OLHS)was performed based on the distribution of uncertainty parameters.The failure criterion of the crankshaft was determined based on the fourth strength theory,and the reliability and failure probability of the crankshaft were predicted by the RBFNN-Monte Carlo(RBFNN-MC)method with the introduction of the PSO.Results show that compared with traditional reliability analysis methods,the RBFNN-MC method offers higher efficiency and better robustness while ensuring high accuracy.