Linear Static Response of Suspension Arm based on Artificial Neural Network Technique
(0) Modeling and simulation are indispensable when dealing with complex engineering systems. This study deals with intelligent techniques modeling for 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 linear static analysis was performed utilizing the finite element analysis code. The neural network model has 3 inputs representing the load, mesh size and material while 4 output representing the maximum displacement, maximum Principal stress, von Mises and Tresca. Finally, regression analysis between finite element results and values predicted by the neural network model was made. It can be seen that the RBFNN proposed approach was found to be highly effective with least error in identification of stress-displacement 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-displacement response of suspension arm as FE methods usually deal with only a single problem for each run.
FEM Suspension arm RBFNN Displacement Stress
M.M. Rahman Hemin M. Mohyaldeen M.M. Noor K. Kadirgama Rosli A. Bakar
Vacuity of Mechanical Engineering, Universiti Malaysia Pahang, 26300 Gambang, Kuantan,Pahang, Malays Vacuity of Mechanical Engineering, Universiti Malaysia Pahang, 26300 Gambang, Kuantan,Pahang, Malays
国际会议
2011 International Conference on Advanced Material Research(ICAMR 2011)(2011年先进材料研究国际会议)
重庆
英文
419-426
2011-01-21（万方平台首次上网日期，不代表论文的发表时间）