Apply Artificial Neural Networks to Estimate Stress Distribution around Holes Edge in Composites

2012 ◽  
Vol 490-495 ◽  
pp. 3105-3108
Author(s):  
Kamran Pazand ◽  
Younes Alizadeh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Composite Laminates ◽  
Composite Plate ◽  
Input Data ◽  
Data Sets ◽  
Fast Determination ◽  
Artificial Neural

The purpose of this paper is to estimate the fast determination of stress distribution around a circular hole in symmetric composite laminates under in-plane loading. For this purpose calculation of stress values in the composite plate around edge holes in different plies position for a finite number of input data sets using the Lekhnitskii expressions and code program. The resulting data would then be used to train artificial neural networks (ANN) which would be able to predict –accurately enough- those quantities throughout the composite plate body for any given input value in any position ply and fore and stress that impose.

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.

Determination of application volume for coffee plantations using artificial neural networks and remote sensing

2021 ◽  
Vol 184 ◽  
pp. 106096
Author(s):  
Mailson Freire de Oliveira ◽  
Adão Felipe dos Santos ◽  
Elizabeth Haruna Kazama ◽  
Glauco de Souza Rolim ◽  
Rouverson Pereira da Silva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Coffee Plantations ◽  
Artificial Neural ◽  
Application Volume

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

scholarly journals The use of classical methods and neural networks in deformation studies of hydrotechnical objects

2020 ◽  
Vol 12 (1) ◽  
pp. 718-725
Author(s):  
Maria Mrówczyńska ◽  
Jacek Sztubecki ◽  
Małgorzata Sztubecka ◽  
Izabela Skrzypczak
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cylinder Surface ◽  
Building Structures ◽  
Steel Cylinder ◽  
Factors Affecting ◽  
Initial Stage ◽  
Artificial Neural ◽  
Set Of Points

Abstract Objects’ measurements often boil down to the determination of changes due to external factors affecting on their structure. The estimation of changes in a tested object, in addition to proper measuring equipment, requires the use of appropriate measuring methods and experimental data result processing methods. This study presents a statement of results of geometrical measurements of a steel cylinder that constitutes the main structural component of the historical weir Czersko Polskie in Bydgoszcz. In the initial stage, the estimation of reliable changes taking place in the cylinder structure involved the selection of measuring points essential for mapping its geometry. Due to the continuous operation of the weir, the points covered only about one-third of the cylinder area. The set of points allowed us to determine the position of the cylinder axis as well as skews and deformations of the cylinder surface. In the next stage, the use of methods based on artificial neural networks allowed us to predict the changes in the tested object. Artificial neural networks have proved to be useful in determining displacements of building structures, particularly hydro-technical objects. The above-mentioned methods supplement classical measurements that create the opportunity for carrying out additional analyses of changes in a spatial position of such structures. The purpose of the tests is to confirm the suitability of artificial neural networks for predicting displacements of building structures, particularly hydro-technical objects.

scholarly journals Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 705
Author(s):  
Josué Trejo-Alonso ◽  
Carlos Fuentes ◽  
Carlos Chávez ◽  
Antonio Quevedo ◽  
Alfonso Gutierrez-Lopez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hydraulic Conductivity ◽  
Input Data ◽  
Logistic Function ◽  
Saturated Hydraulic Conductivity ◽  
Irrigation District ◽  
Pedotransfer Functions ◽  
Back Propagation Algorithm ◽  
Artificial Neural

In the present work, we construct several artificial neural networks (varying the input data) to calculate the saturated hydraulic conductivity (KS) using a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. All of them were constructed using two hidden layers, a back-propagation algorithm for the learning process, and a logistic function as a nonlinear transfer function. In order to explore different arrays for neurons into hidden layers, we performed the bootstrap technique for each neural network and selected the one with the least Root Mean Square Error (RMSE) value. We also compared these results with pedotransfer functions and another neural networks from the literature. The results show that our artificial neural networks obtained from 0.0459 to 0.0413 in the RMSE measurement, and 0.9725 to 0.9780 for R2, which are in good agreement with other works. We also found that reducing the amount of the input data offered us better results.

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