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

2012 ◽  
Keyword(s):  
Thin Films ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Composition ◽  
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

scholarly journals MODELING THE CORRELATION BETWEEN HEAT TREATMENT, CHEMICAL COMPOSITION AND BAINITE FRACTION OF PIPELINE STEELS BY MEANS OF ARTIFICIAL NEURAL NETWORKS

2013 ◽  
Vol 23 (4) ◽  
pp. 351-367 ◽  
Author(s):  
Gholamreza Khalaj ◽  
Hesam Pouraliakbar ◽  
Kaveh Rahimi Mamaghani ◽  
Mohammad-Javad Khalaj
Keyword(s):  
Heat Treatment ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Composition ◽  
Pipeline Steels ◽  
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 Estimation for Thicknesses of Multilayer Nano-Scale Films

2019 ◽  
Vol 962 ◽  
pp. 41-48
Author(s):  
Tzong Daw Wu ◽  
Jiun Shen Chen ◽  
Ching Pei Tseng ◽  
Cheng Chang Hsieh
Keyword(s):  
Thin Films ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Film Growth ◽  
Mean Squared Error ◽  
Squared Error ◽  
Thin Film Optics ◽  
The Mean ◽  
Artificial Neural

This study presents a real-time method for determining the thickness of each layer in multilayer thin films. Artificial neural networks (ANNs) were introduced to estimate thicknesses from a transmittance spectrum. After training via theoretical spectra which were generated by thin-film optics and modified by noise, ANNs were applied to estimate the thicknesses of four-layer nanoscale films which were TiO2, Ag, Ti, and TiO2 thin films assembled sequentially on polyethylene terephthalate (PET) substrates. The results reveal that the mean squared error of the estimation is 2.6 nm2, and is accurate enough to monitor film growth in real time.

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.

WEAR OF RAILWAY TYRE STEELS MODELLING USING ARTIFICIAL NEURAL NETWORKS

Tribologia ◽  
2021 ◽  
Vol 294 (6) ◽  
pp. 77-85
Author(s):  
Mirosław Witaszek ◽  
Kazimierz Witaszek
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Composition ◽  
Sliding Wear ◽  
Sliding Friction ◽  
Narrow Range ◽  
Volume Loss ◽  
Artificial Neural ◽  
Sliding Distance ◽  
Wear Tests

In the paper the results of sliding wear tests were used to model the dependence of steel volume loss on railway wheel tyres on selected material parameters and sliding conditions. The material properties included in this modelling were the hardness and chemical composition of the tyre material (specimens) and the hardness of the mating material (counter-specimens). The conditions for sliding were the initial maximum Hertzian pressure and the sliding distance. The tests were carried out in the ring-block system. Artificial neural networks were used for modelling. It was found that the constructed model made it possible to quantify the volume loss from the above–mentioned factors. A clear influence of the pressure, friction distance, and hardness of both cooperating materials on the studied wear was found. The influence of the chemical composition is less noticeable due to the rather narrow range of its allowable changes. The microscopic tests allowed us to identify the main wear mechanisms in the sliding friction of the tested tyre and rail steels.

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