Linear Static Response of Suspension Arm Based on Artificial Neural Network Technique

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.

[1]  F. Morabito,et al.  A neural network approach for the solution of electric and magnetic inverse problems , 1994 .

[2]  Masato Enokizono,et al.  Natural crack recognition using inverse neural model and multi-frequency eddy current method , 2001 .

[3]  Jooyoung Park,et al.  Approximation and Radial-Basis-Function Networks , 1993, Neural Computation.

[4]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[5]  Abdullah H. Abdullah,et al.  RBFNN Model for Predicting Nonlinear Response of Uniformly Loaded Paddle Cantilever , 2009 .

[6]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[7]  M. Napolitano,et al.  A new learning algorithm for neural network state estimation in active vibration control , 1992 .

[8]  James D. Keeler,et al.  Layered Neural Networks with Gaussian Hidden Units as Universal Approximations , 1990, Neural Computation.

[9]  Erkan Besdok,et al.  A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification , 2009, Sensors.

[10]  Lalita Udpa,et al.  Electromagnetic NDE signal inversion by function-approximation neural networks , 2002 .

[11]  Abhijit S. Pandya,et al.  Pattern Recognition with Neural Networks in C++ , 1995 .

[12]  Prabhat Hajela,et al.  NEURAL NETWORK BASED SELECTION OF DYNAMIC SYSTEM PARAMETERS , 1993 .

[13]  Dan Simon,et al.  Training radial basis neural networks with the extended Kalman filter , 2002, Neurocomputing.

[14]  Mohamad T. Musavi,et al.  On the training of radial basis function classifiers , 1992, Neural Networks.