scholarly journals Evaluation of Seismic Risk for Bridges using F-R-M Method: A Combination of Finite Element Method and Neural Network Model

2017 ◽  
Author(s):  
Mi Zhou ◽  
Wei Feng ◽  
Shuan-hai He
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Finite Element ◽  
Network Model ◽  
Neural Network Model ◽  
Seismic Risk ◽  
Element Method

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

2011 ◽  
Vol 213 ◽  
pp. 419-426
Author(s):  
M.M. Rahman ◽  
Hemin M. Mohyaldeen ◽  
M.M. Noor ◽  
K. Kadirgama ◽  
Rosli A. Bakar
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Network Model ◽  
Neural Network Model ◽  
Mesh Size ◽  
Maximum Displacement ◽  
Element Analysis ◽  
The Neural Network ◽  
The Finite Element Analysis

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.

Multi-physics Analysis of Multi-chip LED Light Source Based on Finite Element Method and Artificial Neural Network

2019 ◽  
Vol 40 (6) ◽  
pp. 795-802 ◽  
Author(s):  
刘宏伟 LIU Hong-wei ◽  
牛萍娟 YU Dan-dan ◽  
郭 凯 NIU Ping-juan ◽  
张建新 ZHANG Zan-yun ◽  
王 闯 GUO Kai ◽  
...  
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Light Source ◽  
Led Light ◽  
Artificial Neural ◽  
Element Method

Optimization of Pressure Path in Sheet Hydroforming Process Using Artificial Intelligence and Simulated Annealing

2014 ◽  
Vol 622-623 ◽  
pp. 772-779 ◽  
Author(s):  
Amirreza Yaghoobi ◽  
Mohammad Bakhshi-Jooybari ◽  
Abdolhamid Gorji ◽  
Hamid Baseri
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Simulated Annealing ◽  
Network Model ◽  
Neural Network Model ◽  
Critical Region ◽  
Element Model ◽  
Sheet Hydroforming ◽  
The Neural Network ◽  
Hydroforming Process

The success of sheet hydroforming process largely depends on the loading pressure path. Pressure path is one of the most important parameters in sheet hydroforming process. In this study, a combination of finite element simulation, artificial intelligence and simulated annealing optimization have been utilized to optimize the pressure path in producing cylindrical-spherical parts. In the beginning, the finite element model was verified based on laboratory experimental results. The experiments were designed and a radial basis neural network model was developed using data generated from verified finite element model to predict the thickness in the critical region of the product. Results indicated that the neural network model could be applied successfully to predict the sheet thickness in the critical region. In addition, the neural network model was used as a fitness function in simulated annealing algorithm to minimize the thickening in the above mentioned critical region. The final results showed that utilization of the optimized pressure path yields good thickness distribution of the part.

Sign in / Sign up

Export Citation Format

Share Document