Investigate of Linear Response of Vehicle Arm Based on Artificial Intelligence Technique

Abstract

Neural networks have emerged as a field of study within artificial intelligent AI and engineering via the collaborative efforts of engineers, physicists, mathematicians, computer scientists, and neuroscientists. This study deals with intelligent technique's modeling for a linear response of suspension arm. The finite element analysis and Radial Basis Function Neural Network (RBFNN) technique is used to predict the response of suspension arm. The neural network model has three inputs representing the load, mesh size and material while three output representing the maximum principal stress, Von Mises and Tresca. Regression analysis between finite element results and values predicted by the neural network model was made, and RBFNN proposed approach was found to be highly effective with least error in identification of stress of suspension arm. Simulated results show that RBF can be very successively used for reduction of the effort and time required to predict the stress response of suspension arm as FE methods usually deal with only a single problem for each run.