Computing Method and Circuit Realization of Neural Network on Finite Element Analysis

2012 ◽  
Vol 195-196 ◽  
pp. 758-763
Author(s):  
Li Kun Cui ◽  
Wei Wang ◽  
Zhuo Li
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Computing Time ◽  
Hopfield Neural Network ◽  
Theory Of Elasticity ◽  
Practical Significance ◽  
Stable Equilibrium Point ◽  
Element Analysis ◽  
Circuit Realization

The finite element analysis in theory of elasticity is corresponded to the quadratic programming with equality constraint, which can be further transformed into the unconstrained optimization. In the paper, the question is solved by modified Hopfield neural network based on the energy function of the neural network equals to the objective function of the finite element method and the minimum point, which is the stable equilibrium point of the network system, is the solution. In addition the authors present the computer simulation and analogue circuit experiment to verify this method. The results are revealed that: 1) The results of improved Hopfield neural network are reliable and accuracy; 2) The improved Hopfield neural network model has an advantage on circuit realization and the computing time, which is unrelated with complexity of the structure, is constant. It is practical significance for the research and calculation.

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.

A Non-Cyclic Method for Plastic Shakedown Analysis

2003 ◽  
Author(s):  
W. Reinhardt
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Computing Time ◽  
Strain Accumulation ◽  
Analysis Method ◽  
Element Analysis ◽  
The Finite Element Analysis ◽  
Cyclic Phenomenon ◽  
Elastic Shakedown ◽  
Plastic Shakedown

Shakedown is a cyclic phenomenon, and for its analysis it seems natural to employ a cyclic analysis method. Two problems are associated when this direct approach is used in finite element analysis. Firstly, the analysis typically needs to be stabilized over several cycles, and the analysis of each individual cycle may need a considerable amount of computing time. Secondly, even in cases where a stable cycle is known to exist, the finite element analysis can show a small continuing amount of strain accumulation. For elastic shakedown, non-cyclic analysis methods that use Melan’s theorem have been proposed. The present paper extends non-cyclic lower bound methods to the analysis of plastic shakedown. The proposed method is demonstrated with several example problems.

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

scholarly journals Efficient analysis of welding thermal conduction using the Newton–Raphson method, implicit method, and their combination

2020 ◽  
Vol 111 (7-8) ◽  
pp. 1929-1940 ◽  
Author(s):  
Zhongyuan Feng ◽  
Ninshu Ma ◽  
Wangnan Li ◽  
Kunio Narasaki ◽  
Fenggui Lu
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Hybrid Method ◽  
Thermal Conduction ◽  
Computing Time ◽  
Implicit Method ◽  
Heating Process ◽  
Element Analysis ◽  
Heating And Cooling ◽  
Raphson Method

AbstractFinite element analysis is commonly used to investigate the thermal-mechanical phenomena during welding. To improve the computing efficiency of finite element analysis for welding thermal conduction, a novel Newton–Raphson method (NRM) without the computation of inverse matrix and a hybrid method combing the NRM and conventional implicit method (IMP) were developed. Comparison of computing time between the hybrid method implemented in an in-house software JWRIAN and the IMP used in a commercial software ABAQUS indicated that the computing speed of the former was about 4.5 times faster than that of the latter. Additionally, compared to the conventional IMP, the NRM exhibited higher computing efficiency in the analysis of transient thermal conduction during the welding heating process. Meanwhile, a combined hybrid method of the NRM and IMP was verified to be more efficient in analyzing the welding thermal conduction throughout the heating and cooling processes. Moreover, the thermal cycles computed by the hybrid method were consistent with those from experimental measurement, indicating the high accuracy of the hybrid method. Furthermore, the hybrid method was used to predict the temperature field of the corner boxing fillet joint welded by a low transformation temperature weld metal for generation of compressive residual stress.

RPC Pole Composite Cross Arm Design and Wind-Induced Dynamic Analysis

2014 ◽  
Vol 919-921 ◽  
pp. 280-283
Author(s):  
Yan Zhong Ju ◽  
Nian Tong ◽  
Qi Wu
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Composite Materials ◽  
Dynamic Response ◽  
Transmission Lines ◽  
High Strength ◽  
Practical Significance ◽  
Analysis Software ◽  
Element Analysis ◽  
Fluctuating Wind

The author puts forward a new insulation cross arm, uses the insulation and high strength composite materials (FRP), to replace the steel applied to the RPC pole cross arm, this not only has a full bar maintenance-free features, and can shorten transmission corridor again, for the construction of resource intensive transmission lines has important practical significance. In addition, this paper uses finite element analysis software ANSYS, considering the influence of fluctuating wind, in the modal analysis and dynamic response illustrates the actuality of cross arm application of composite materials.

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