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

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

Neural Network World ◽

10.14311/nnw.2013.23.022 ◽

2013 ◽

Vol 23 (4) ◽

pp. 351-367 ◽

Cited By ~ 12

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

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Ozone Concentration Prediction using Artificial Neural Networks

Revista de Chimie ◽

10.37358/rc.17.10.5860 ◽

2017 ◽

Vol 68 (10) ◽

pp. 2224-2227 ◽

Cited By ~ 2

Author(s):

Camelia Gavrila

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Ozone Concentration ◽

Meteorological Data ◽

Global Solar Radiation ◽

Time Data ◽

Marquardt Algorithm ◽

Sensitivity Tests ◽

Artificial Neural ◽

The Relationship

The aim of this paper is to determine a mathematical model which establishes the relationship between ozone levels together with other meteorological data and air quality. The model is valid for any season and for any area and is based on real-time data measured in Bucharest and its surroundings. This study is based on research using artificial neural networks to model nonlinear relationships between the concentration of immission of ozone and the meteorological factors: relative humidity (RH), global solar radiation (SR), air temperature (TEMP). The ozone concentration depends on following primary pollutants: nitrogen oxides (NO, NO2), carbon monoxide (CO). To achieve this, the Levenberg-Marquardt algorithm was implemented in Scilab, a numerical computation software. Performed sensitivity tests proved the robustness of the model and its applicability in predicting the ozone on short-term.

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Evaluation and Modeling of Passenger Vehicle Celeration Performance Based on Artificial Neural Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.744-746.1938 ◽

2015 ◽

Vol 744-746 ◽

pp. 1938-1942

Author(s):

Yi He ◽

Duan Feng Chu

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Comfort Level ◽

Subjective Rating ◽

Acceleration Data ◽

Acceleration And Deceleration ◽

The Subject ◽

Artificial Neural ◽

The Relationship ◽

Subjective Sensation

As the siginificant factors influence passengers comfort, the vehicle celebration performance may easy to cause accidents, such as hard acceleration and deceleration performance. In order to find the relationship between passengers comfort and celebration performance, 35 passengers and three professional drivers were recruited in the field experiment. The passengers’ comfort feelings were analysed by subject questionnaires, the acceleration and deceleration data were received by CAN bus.The Artificial Neural Networks (ANNs) model was elaborated to estimate and predict the passengers comfort level of driver unsafe acceleration behavior situations. Therefore, the subject views of the passengers could be compared to object acceleration data. An ANN is applied to interconnect output data (subjective rating) with input data (objective parameters). Finally, it is found the investigatioin have demonstrated that the objective values are efficiently correlated with the subjective sensation. Thus, the presented approach can be effectively applied to support the drive train development of bus.

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Intelligence Selection System for Honing Parameter Based on Genetics and Artificial Neural Networks

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.102-104.846 ◽

2010 ◽

Vol 102-104 ◽

pp. 846-850

Author(s):

Wen Yu Pu ◽

Yan Nian Rui ◽

Lian Sheng Zhao ◽

Chun Yan Zhang

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Selection System ◽

Process Data ◽

Replacement Strategy ◽

Machining Quality ◽

Main Factors ◽

Artificial Neural ◽

Neural Networks Optimization ◽

The Relationship

Appropriate selecting of process parameters influences the machining quality greatly. For honing, the main factors are product precision, material components and productivity. In view of this situation, a intelligence selection model for honing parameter based on genetics and artificial neural networks was built by using excellent robustness, fault-tolerance of artificial neural networks optimization process and excellent self-optimum of genetic algorithm. It can simulate the decision making progress of experienced operators, abstract the relationship from process data and machining incidence, realize the purpose of intelligence selection honing parameter through copying, exchanging, aberrance, replacement strategy and neural networks training. Besides, experiment was performed and the results helped optimize the theories model. Both the theory and experiment show the updated level and feasibility of this system.

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

Aceh International Journal of Science and Technology ◽

10.13170/aijst.7.3.11327 ◽

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.

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