Numerical implementation of a neural network based material model in finite element analysis

2004 ◽  
Vol 59 (7) ◽  
pp. 989-1005 ◽  
Author(s):  
Y. M. A. Hashash ◽  
S. Jung ◽  
J. Ghaboussi
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Material Model ◽  
Numerical Implementation ◽  
Element Analysis

Lifetime Prediction of Tires with Regard to Oxidative Aging5

2008 ◽  
Vol 36 (1) ◽  
pp. 63-79 ◽  
Author(s):  
L. Nasdala ◽  
Y. Wei ◽  
H. Rothert ◽  
M. Kaliske
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Temperature Distribution ◽  
Superposition Principle ◽  
Accelerated Aging ◽  
Material Model ◽  
Aging Behavior ◽  
Element Analysis ◽  
Material Parameters ◽  
Reaction Processes

Abstract It is a challenging task in the design of automobile tires to predict lifetime and performance on the basis of numerical simulations. Several factors have to be taken into account to correctly estimate the aging behavior. This paper focuses on oxygen reaction processes which, apart from mechanical and thermal aspects, effect the tire durability. The material parameters needed to describe the temperature-dependent oxygen diffusion and reaction processes are derived by means of the time–temperature–superposition principle from modulus profiling tests. These experiments are designed to examine the diffusion-limited oxidation (DLO) effect which occurs when accelerated aging tests are performed. For the cord-reinforced rubber composites, homogenization techniques are adopted to obtain effective material parameters (diffusivities and reaction constants). The selection and arrangement of rubber components influence the temperature distribution and the oxygen penetration depth which impact tire durability. The goal of this paper is to establish a finite element analysis based criterion to predict lifetime with respect to oxidative aging. The finite element analysis is carried out in three stages. First the heat generation rate distribution is calculated using a viscoelastic material model. Then the temperature distribution can be determined. In the third step we evaluate the oxygen distribution or rather the oxygen consumption rate, which is a measure for the tire lifetime. Thus, the aging behavior of different kinds of tires can be compared. Numerical examples show how diffusivities, reaction coefficients, and temperature influence the durability of different tire parts. It is found that due to the DLO effect, some interior parts may age slower even if the temperature is increased.

Elastic and elastic-plastic finite element analysis of hollow tubes with axisymmetric internal projections under combined axial and pressure loading

2001 ◽  
Vol 36 (4) ◽  
pp. 373-390 ◽  
Author(s):  
S. J Hardy ◽  
M. K Pipelzadeh ◽  
A. R Gowhari-Anaraki
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Internal Pressure ◽  
Plastic Material ◽  
Axial Loading ◽  
Material Model ◽  
Pressure Loading ◽  
Element Analysis ◽  
Elastic Plastic ◽  
Applied Loading

This paper discusses the behaviour of hollow tubes with axisymmetric internal projections subjected to combined axial and internal pressure loading. Predictions from an extensive elastic and elastic-plastic finite element analysis are presented for a typical geometry and a range of loading combinations, using a simplified bilinear elastic-perfectly plastic material model. The axial loading case, previously analysed, is extended to cover the additional effect of internal pressure. All the predicted stress and strain data are found to depend on the applied loading conditions. The results are normalized with respect to material properties and can therefore be applied to geometrically similar components made from other materials, which can be represented by the same material models.

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.

Prediction of Cutting Force in Bone Cutting Using Finite Element Analysis

2021 ◽  
Author(s):  
Varatharajan Prasannavenkadesan ◽  
Ponnusamy Pandithevan
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Cutting Force ◽  
Material Model ◽  
Tensile Tests ◽  
Bovine Bone ◽  
Element Analysis ◽  
Cutting Processes ◽  
Parameter Values ◽  
First Time

Abstract In orthopedic surgery, bone cutting is an indispensable procedure followed by the surgeons to treat the fractured and fragmented bones. Because of the unsuitable parameter values used in the cutting processes, micro crack, fragmentation, and thermal osteonecrosis of bone are observed. Therefore, prediction of suitable cutting force is essential to subtract the bone without any adverse effect. In this study, the Cowper-Symonds model for bovine bone was developed for the first time. Then the developed model was coupled with the finite element analysis to predict the cutting force. To determine the model constants, tensile tests with different strain rates (10−5/s, 10−4/s, 10−3/s, and 1/s) were conducted on the cortical bone specimens. The developed material model was implemented in the bone cutting simulation and validated with the experiments.

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

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