Finite element analysis and neural network model for electronic hidden solder joint geometry prediction

2010 ◽  
Author(s):  
Jose Benjamin Dolores Giron Palomares ◽  
Sheng-Jen Hsieh
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Solder Joint ◽  
Network Model ◽  
Neural Network Model ◽  
Element Analysis ◽  
Joint Geometry

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.

Vibration–Based Damage Detection Of Slab Structure Using Artificial Neural Network

2012 ◽  
Author(s):  
Norhisham Bakhary
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Damage Detection ◽  
Ann Model ◽  
Element Analysis ◽  
Severity Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kertas kerja ini memaparkan kajian berkenaan keberkesanan Artificial Neural Network (ANN) dalam mengenal pasti kerosakan di dalam struktur. Data dari getaran seperti frekuensi semula jadi dan mod bentuk digunakan sebagai data masukan bagi ANN untuk meramalkan lokasi dan tahap kerosakan bagi struktur lantai. Analisis unsur terhingga (Finite Element Analysis) telah digunakan untuk memperoleh ciri–ciri dinamik bagi struktur–struktur rosak dan tidak rosak untuk ‘melatih’ model ‘neural network’. Senario kerosakan yang berbeza disimulasikan dengan mengurangkan kekukuhan elemen pada lokasi yang berbeza sepanjang struktur tersebut. Berbagai kombinasi data masukan bagi mod yang berbeza telah digunakan untuk memperolehi model ANN yang terbaik. Hasil kajian ini menunjukkan ANN mampu memberikan keputusan yang baik dalam meramal kerosakan pada struktur lantai tersebut. Kata kunci: Ramalan kerosakan struktur, Artificial Neural Network This paper investigates the effectiveness of artificial neural network (ANN) in identifying damages in structures. Global (natural frequencies) and local (mode shapes) vibration–based data has been used as the input to ANN for location and severity prediction of damages in a slab–like structure. A finite element analysis has been used to obtain the dynamic characteristics of intact and damaged structure to train the neural network model. Different damage scenarios have been introduced by reducing the local stiffness of the selected elements at different locations along the structure. Several combinations of input variables in different modes have been used in order to obtain a reliable ANN model. The trained ANN model is validated using laboratory test data. The results show that ANN is capable of providing acceptable result on damage prediction of tested slab structure. Key words: Structural damage detection, artificial neural network

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.

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