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

M. M., Noor and M. M., Rahman and R. A., Bakar and K., Kadirgama (2011) Linear Static Response of Suspension Arm based on Artificial Neural Network Technique. Advanced Materials Research, 213 (2. pp. 419-426. ISSN 1022-6680. (Published)

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Abstract

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.

Item Type:	Article
Additional Information:	Official URL for fulltext Muhamad Mat Noor (M. M. Noor) Prof. Dr. Md Mustafizur Rahman (M. M. Rahman) Prof. Dato’ Dr. Rosli Abu Bakar (R. A. Bakar) Dr. Kumaran Kadirgama (K. Kadirgama)
Uncontrolled Keywords:	FEM; Suspension arm; RBFNN; Displacement; Stress
Subjects:	T Technology > TJ Mechanical engineering and machinery
Faculty/Division:	Faculty of Mechanical Engineering
Depositing User:	Pn. Hazlinda Abd Rahman
Date Deposited:	08 Mar 2012 07:23
Last Modified:	25 Jan 2018 04:23
URI:	http://umpir.ump.edu.my/id/eprint/2217
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