scholarly journals Erratum: “Incorporating Neural Network Material Models Within Finite Element Analysis for Rheological Behavior Prediction” [Journal of Pressure Vessel Technology, 2007, 129(1), pp. 58–65]

2007 ◽  
Vol 129 (2) ◽  
pp. 342-342 ◽  
Author(s):  
B. Scott Kessler ◽  
A. Sherif El-Gizawy ◽  
Douglas E. Smith
Keyword(s):  
Neural Network ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Rheological Behavior ◽  
Pressure Vessel ◽  
Behavior Prediction ◽  
Element Analysis ◽  
Material Models

Comparison of Different Material Models to Simulate 3-D Breast Deformations Using Finite Element Analysis

2013 ◽  
Vol 42 (4) ◽  
pp. 843-857 ◽  
Author(s):  
Maximilian Eder ◽  
Stefan Raith ◽  
Jalil Jalali ◽  
Alexander Volf ◽  
Markus Settles ◽  
...  
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Element Analysis ◽  
Material Models

scholarly journals On the Design of a Biodegradable POC-HA Polymeric Cardiovascular Stent

2012 ◽  
Author(s):  
Joonas Ponkala ◽  
Mohsin Rizwan ◽  
Panos S. Shiakolas
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Material Properties ◽  
Three Dimensional ◽  
Polymeric Composite ◽  
Coronary Stent ◽  
Element Analysis ◽  
Material Models ◽  
Solid Models ◽  
Biodegradable Stents

The current state of the art in coronary stent technology, tubular structures used to keep the lumen open, is mainly populated by metallic stents coated with certain drugs to increase biocompatibility, even though experimental biodegradable stents have appeared in the horizon. Biodegradable polymeric stent design necessitates accurate characterization of time dependent polymer material properties and mechanical behavior for analysis and optimization. This manuscript presents the process for evaluating material properties for biodegradable biocompatible polymeric composite poly(diol citrate) hydroxyapatite (POC-HA), approaches for identifying material models and three dimensional solid models for finite element analysis and fabrication of a stent. The developed material models were utilized in a nonlinear finite element analysis to evaluate the suitability of the POC-HA material for coronary stent application. In addition, the advantages of using femtosecond laser machining to fabricate the POC-HA stent are discussed showing a machined stent. The methodology presented with additional steps can be applied in the development of a biocompatible and biodegradable polymeric stents.

scholarly journals Finite Element Analysis of Skirt to Shell Junction in a Pressure Vessel

2017 ◽  
Vol 10 (25) ◽  
pp. 1-10
Author(s):  
Deepali Mathur ◽  
Mandar Sapre ◽  
Chintan Hingoo ◽  
◽  
◽  
...  
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Pressure Vessel ◽  
Element Analysis

Incorporating Neural Network Material Models Within Finite Element Analysis for Rheological Behavior Prediction

2006 ◽  
Vol 129 (1) ◽  
pp. 58-65 ◽  
Author(s):  
B. Scott Kessler ◽  
A. Sherif El-Gizawy ◽  
Douglas E. Smith
Keyword(s):  
Finite Element ◽  
Effective Means ◽  
Element Model ◽  
Operating Conditions ◽  
Behavior Prediction ◽  
Element Analysis ◽  
Material Models ◽  
Rheological Models ◽  
Wide Range ◽  
Novel Method

The accuracy of a finite element model for design and analysis of a metal forging operation is limited by the incorporated material model’s ability to predict deformation behavior over a wide range of operating conditions. Current rheological models prove deficient in several respects due to the difficulty in establishing complicated relations between many parameters. More recently, artificial neural networks (ANN) have been suggested as an effective means to overcome these difficulties. To this end, a robust ANN with the ability to determine flow stresses based on strain, strain rate, and temperature is developed and linked with finite element code. Comparisons of this novel method with conventional means are carried out to demonstrate the advantages of this approach.

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

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