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...

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Main Authors:	M. M., Noor, M. M., Rahman, R. A., Bakar, K., Kadirgama
Format:	Article
Language:	English
Published:	2011
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/2217/ http://umpir.ump.edu.my/id/eprint/2217/ http://umpir.ump.edu.my/id/eprint/2217/1/Linear_Static_Response_of_Suspension_Arm_based_on_Artificial_Neural.pdf

id	ump-2217
recordtype	eprints
spelling	ump-22172018-01-25T04:23:19Z http://umpir.ump.edu.my/id/eprint/2217/ Linear Static Response of Suspension Arm based on Artificial Neural Network Technique M. M., Noor M. M., Rahman R. A., Bakar K., Kadirgama TJ Mechanical engineering and machinery 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. 2011 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/2217/1/Linear_Static_Response_of_Suspension_Arm_based_on_Artificial_Neural.pdf 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 http://www.scientific.net/AMR.213.419
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery M. M., Noor M. M., Rahman R. A., Bakar K., Kadirgama Linear Static Response of Suspension Arm based on Artificial Neural Network Technique
description	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.
format	Article
author	M. M., Noor M. M., Rahman R. A., Bakar K., Kadirgama
author_facet	M. M., Noor M. M., Rahman R. A., Bakar K., Kadirgama
author_sort	M. M., Noor
title	Linear Static Response of Suspension Arm based on Artificial Neural Network Technique
title_short	Linear Static Response of Suspension Arm based on Artificial Neural Network Technique
title_full	Linear Static Response of Suspension Arm based on Artificial Neural Network Technique
title_fullStr	Linear Static Response of Suspension Arm based on Artificial Neural Network Technique
title_full_unstemmed	Linear Static Response of Suspension Arm based on Artificial Neural Network Technique
title_sort	linear static response of suspension arm based on artificial neural network technique
publishDate	2011
url	http://umpir.ump.edu.my/id/eprint/2217/ http://umpir.ump.edu.my/id/eprint/2217/ http://umpir.ump.edu.my/id/eprint/2217/1/Linear_Static_Response_of_Suspension_Arm_based_on_Artificial_Neural.pdf
first_indexed	2023-09-18T21:55:48Z
last_indexed	2023-09-18T21:55:48Z
_version_	1777414070762209280

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

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