Artificial neural networks for hardness prediction of HAZ with chemical composition and tensile test of X70 pipeline steels

2015 ◽  
Vol 22 (5) ◽  
pp. 446-450 ◽  
Author(s):  
Hesam Pouraliakbar ◽  
Mohammad-javad Khalaj ◽  
Mohsen Nazerfakhari ◽  
Gholamreza Khalaj
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Composition ◽  
Tensile Test ◽  
Pipeline Steels ◽  
Hardness Prediction ◽  
Artificial Neural

scholarly journals Atmospheric Pollutant Flow and Precipitation: Modeling Effects on the Vegetation Ecosystem

2018 ◽  
Vol 7 (3) ◽  
pp. 157-161
Author(s):  
Allag Fateh ◽  
Saddek Bouharati ◽  
Lamri Tedjar ◽  
Mohamed Fenni
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Composition ◽  
Wide Distribution ◽  
Climatic Conditions ◽  
Mathematical Methods ◽  
Atmospheric Pollutant ◽  
Artificial Neural ◽  
Precipitation Modeling ◽  
The Impact

Because of their fixed life and wide distribution, plants are the first victims of air pollution. The atmosphere is considered polluted when the increase of the rate of certain components causes harmful effects on the different constituents of the ecosystems. The study of the flow of air near a polluting source (cement plant in our case), allows to predict its impact on the surrounding plant ecosystem. Different factors are to be considered. The chemical composition of the air, the climatic conditions, and the impacted plant species are complex parameters to be analyzed using conventional mathematical methods. In this study, we propose a system based on artificial neural networks. Since artificial neural networks have the capacity to treat different complex parameters, their application in this domain is adequate. The proposed system makes it possible to match the input and output spaces. The variables that constitute the input space are the chemical composition, the concentration of the latter in the rainwater, their duration of deposition on the leaves and stems, the climatic conditions characterizing the environment, as well as the species of plant studied. The output variable expresses the rate of degradation of this species under the effect of pollution. Learning the system makes it possible to establish the transfer function and thus predict the impact of pollutants on the vegetation.

Artificial Neural Networks Approach for Hardness Prediction of Copper Cold Spray Laser Heat Treated Coatings

2022 ◽  
Author(s):  
Maryam Razavipour ◽  
Jean-Gabriel Legoux ◽  
Dominique Poirier ◽  
Bruno Guerreiro ◽  
Jason D. Giallonardo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cold Spray ◽  
Laser Heat ◽  
Heat Treated ◽  
Hardness Prediction ◽  
Artificial Neural

Application of Artificial Neural Networks to Predict Chemical Composition of Electrodeposited Nanocrystalline Ni-Mo Thin Films

2013 ◽  
Vol 50 (52) ◽  
pp. 63-71
Author(s):  
M. H. Allahyarzadeh ◽  
A. Ashrafi ◽  
T. Shahrabi ◽  
A. Seddighian ◽  
M. Aliofkhazraei ◽  
...  
Keyword(s):  
Thin Films ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Composition ◽  
Artificial Neural

Damage Mechanisms Identification in FRP Using Acoustic Emission and Artificial Neural Networks

2006 ◽  
Vol 514-516 ◽  
pp. 789-793 ◽  
Author(s):  
Rui de Oliveira ◽  
António Torres Marques
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Artificial Neural Networks ◽  
Tensile Test ◽  
A Priori ◽  
The Self ◽  
A Priori Knowledge ◽  
Self Organizing Maps ◽  
Damage Mechanisms ◽  
Artificial Neural

In this study is proposed a procedure for damage discrimination based on acoustic emission signals clustering using artificial neural networks. An unsupervised methodology based on the self-organizing maps of Kohonen is developed considering the lack of a priori knowledge of the different signal classes. The methodology is described and applied to a cross-ply glassfibre/ polyester laminate submitted to a tensile test. In this case, six different AE waveforms were identified. The damage sequence could so be identified from the modal nature of those waves.

Modelling of Hardness Prediction of Alloyed Copper Using Artificial Neural Networks Applications

2014 ◽  
Vol 1036 ◽  
pp. 52-57
Author(s):  
Jarosław Konieczny ◽  
Blazej Chmielnicki ◽  
Błażej Tomiczek
Keyword(s):  
Heat Treatment ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Composition ◽  
Cold Working ◽  
Calculation Model ◽  
Chemical Compositions ◽  
Deformation Degree ◽  
Artificial Neural ◽  
The Relationship

The aim of the work is to employ the artificial neural networks for prediction of hardness of the alloyed copper like CuTi, CuFe, CuCr and CuNiSi. In this paper it has been presented an original trial of prediction of the required hardness of the alloyed copper like CuTi, CuFe, CuCr and CuNiSi. Artificial neural networks, can be applied for predicting the effect of the chemical composition, parameters of heat treatment and cold working deformation degree on the hardness. It has been assumed that the artificial neural networks can be used to assign the relationship between the chemical compositions of alloyed copper, temperature and time of solution heat treatment, degree of cold working deformation and temperature and time of ageing. In order to determine the relationship it has been necessary to work out a suitable calculation model. It has been proved that employment of genetic algorithm to selection of input neurons can be very useful tool to improve artificial neural network calculation results. The attempt to use the artificial neural networks for predicting the effect of the chemical composition and parameters of heat treatment and cold working deformation degree on the hardness succeeded, as the level of the obtained results was acceptable. Worked out model should be used for prediction of hardness only in particular groups of alloyed copper, mostly because of the discontinuous character of input data. The results of research make it possible to calculate with a certain admissible error the hardness value basing on combinations of concentrations of the particular elements, heat treatment parameters and cold working deformation degree.

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