Prediction of atmospheric corrosion of carbon steel using artificial neural network model in local geographical regions
Halama, M. Kreislova, K. Van Lysebettens, J.
Prediction of atmospheric corrosion of carbon steel using artificial neural network model in local geographical regions - jun. 2011 - 6 p. ; 065004
Transcripción del resumen del autor. Atmospheric corrosion of metals is a complex, nonlinear process. It involves a large number of interacting and varying factors governed by material composition, form, size, testing procedure, location of exposure, and type of application. Possible environmental factors include temperature, relative humidity, wet-dry patterns, hours of sunshine, pH of rainfall, amount of precipitation, concentration of main pollutants, etc. All factors are apart of the variables in artificial neural network (ANN) modeling. The most important variables are chosen from long-term experiences as elements in the development of a prototype "artificial intelligent sensor"'a model designed for the assessment of atmospheric corrosion of carbon steel under local geographical conditions. The variable impact analysis and 2D maps gave accurate prediction of the atmospheric corrosion of carbon steel. Future climatic scenarios, mainly the calculation of mass losses under different simulated concentrations of sulfur dioxide (SO2) during exposure time, are presented.
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Prediction of atmospheric corrosion of carbon steel using artificial neural network model in local geographical regions - jun. 2011 - 6 p. ; 065004
Transcripción del resumen del autor. Atmospheric corrosion of metals is a complex, nonlinear process. It involves a large number of interacting and varying factors governed by material composition, form, size, testing procedure, location of exposure, and type of application. Possible environmental factors include temperature, relative humidity, wet-dry patterns, hours of sunshine, pH of rainfall, amount of precipitation, concentration of main pollutants, etc. All factors are apart of the variables in artificial neural network (ANN) modeling. The most important variables are chosen from long-term experiences as elements in the development of a prototype "artificial intelligent sensor"'a model designed for the assessment of atmospheric corrosion of carbon steel under local geographical conditions. The variable impact analysis and 2D maps gave accurate prediction of the atmospheric corrosion of carbon steel. Future climatic scenarios, mainly the calculation of mass losses under different simulated concentrations of sulfur dioxide (SO2) during exposure time, are presented.
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