An Inverse Method of Load Determination Using Hybrid Genetic Algorithms, Artificial Neural Networks and Finite Element Analysis

2009 ◽  
Author(s):  
M. Rabbani ◽  
S. Noroozi ◽  
J. Vinney ◽  
S. Shirazi Kia
Keyword(s):  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Inverse Method ◽  
Element Analysis ◽  
Hybrid Genetic Algorithms ◽  
Artificial Neural

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

scholarly journals A Review of Finite Element Analysis and Artificial Neural Networks as Failure Pressure Prediction Tools for Corroded Pipelines

Materials ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6135
Author(s):  
Suria Devi Vijaya Kumar ◽  
Michael Lo Yin Kai ◽  
Thibankumar Arumugam ◽  
Saravanan Karuppanan
Keyword(s):  
Neural Networks ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Structural Integrity ◽  
The Finite Element Method ◽  
Element Analysis ◽  
Strength Assessment ◽  
Failure Pressure ◽  
Prediction Tools ◽  
Artificial Neural

This paper discusses the capabilities of artificial neural networks (ANNs) when integrated with the finite element method (FEM) and utilized as prediction tools to predict the failure pressure of corroded pipelines. The use of conventional residual strength assessment methods has proven to produce predictions that are conservative, and this, in turn, costs companies by leading to premature maintenance and replacement. ANNs and FEM have proven to be strong failure pressure prediction tools, and they are being utilized to replace the time-consuming methods and conventional codes. FEM is widely used to evaluate the structural integrity of corroded pipelines, and the integration of ANNs into this process greatly reduces the time taken to obtain accurate results.

A Neurogenetic Approach to a Multiobjective Design Optimization of Spinal Pedicle Screws

2010 ◽  
Vol 132 (9) ◽  
Author(s):  
Ching-Kong Chao ◽  
Jinn Lin ◽  
Sandy Tri Putra ◽  
Ching-Chi Hsu
Keyword(s):  
Neural Networks ◽  
Finite Element ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Pedicle Screw ◽  
Pedicle Screws ◽  
Screw Breakage ◽  
Artificial Neural ◽  
Multiobjective Design

A pedicle screw fixation has been widely used to treat spinal diseases. Clinical reports have shown that the weakest part of the spinal fixator is the pedicle screw. However, previous studies have only focused on either screw breakage or screw loosening. There have been no studies that have addressed the multiobjective design optimization of the pedicle screws. The multiobjective optimization methodology was applied and it consisted of finite element method, Taguchi method, artificial neural networks, and genetic algorithms. Three-dimensional finite element models for both the bending strength and the pullout strength of the pedicle screw were first developed and arranged on an L25 orthogonal array. Then, artificial neural networks were used to create two objective functions. Finally, the optimum solutions of the pedicle screws were obtained by genetic algorithms. The results showed that the optimum designs had higher bending and pullout strengths compared with commercially available screws. The optimum designs of pedicle screw revealed excellent biomechanical performances. The neurogenetic approach has effectively decreased the time and effort required for searching for the optimal designs of pedicle screws and has directly provided the selection information to surgeons.

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