Elastic constants of austenitic stainless steel: Investigation by the first-principles calculations and the artificial neural network approach
ثبت نشده
چکیده
Résumé : In this paper, two methods were applied to determine the different elastics constants of the face centered cubic austenitic stainless steel Fe0.62Cr0.185Ni0.185. Firstly, the quantum mechanical simulation was applied based on the first principles calculations within the generalized gradient approximation (GGA) by using the efficient strain-stress method. Secondly an artificial neural network (ANN) is used based on back propagation algorithm training. ANN model has been developed for the analysis and simulation of the correlation between the elastic properties and composition. In the training model three input layers each accept the weight percentage of the alloy component (Fe, Cr and Ni), while the three different elastics constants c(11), c(12) and c(44) were employed as outputs. Different models of ANN were developed to predict the elastic constants. The performance indices such as coefficient of determination, mean square error were used to control the performance of the prediction capacity of the models developed in this study. In addition to this, elastic constants obtained from ANN models were compared with those obtained from quantum mechanical simulation and with those reported in the literature. The prediction results obtained by the two methods seem to be satisfactory. (C) 2012 Elsevier B.V. All rights reserved. Keywords/Mots cléfs : Journal title / Revue : Elastic constants of austenitic stainless steel: Investigation by the first-principles calculations and the artificial neural network approach , 0927-0256, "DOI" , 10.1016/j.commatsci.2012.09.005, "issue" , "volume" , 67 , "pp" 353 358, 25 FEB 2013 Source: COMPUTATIONAL MATERIALS SCIENCE
منابع مشابه
Artificial neural network models for production of nano-grained structure in AISI 304L stainless steel by predicting thermo-mechanical parameters
An artificial neural network (ANN) model is developed for the analysis, simulation, and prediction of the austenite reversion in the thermo-mechanical treatment of 304L austenitic stainless steel. The results of the ANN model are in good agreement with the experimental data. The model is used to predict an appropriate annealing condition for austenite reversion through the martensite to austeni...
متن کاملPrediction of Time to Failure in SCC of 304 Stainless Steel in Aqueous Chloride Solution Using Neural Network
Prediction of SCC risk of austenitic stainless steels in aqueous chloride solution and estimation of the time to failure as a result of SCC form important and complicated topics for study. Despite the many studies reported in the literature, a formulation or a reliable method for the prediction of time to failure as a result of SCC is yet to be developed. This paper is an effort to investigat...
متن کاملPrediction of Time to Failure in SCC of 304 Stainless Steel in Aqueous Chloride Solution Using Neural Network
Prediction of SCC risk of austenitic stainless steels in aqueous chloride solution and estimation of the time to failure as a result of SCC form important and complicated topics for study. Despite the many studies reported in the literature, a formulation or a reliable method for the prediction of time to failure as a result of SCC is yet to be developed. This paper is an 
effort to investig...
متن کاملRole of Artificial Neural Network in Welding Technology: A Survey
Parikshik dutta, Dilip Kumar pratihar do modelling of TIF welding process using conventional regression analysis and neural network-based approaches. journal of Materials Processing Technology 184 (2007) 56–68 P. Sathiya ?, K. Panneerselvam, M. Y. Abdul Jaleel "Optimization of laser welding process parameters for super austenitic stainless steel using artificial neural networks and genetic...
متن کاملPrediction of Mechanical Properties of TWIP Steels using Artificial Neural Network Modeling
In recent years, great attention has been paid to the development of high manganese austenitic TWIP steels exhibiting high tensile strength and exceptional total elongation. Due to low stacking fault energy (SFE), cross slip becomes more difficult in these steels and mechanical twinning is then the favored deformation mode besides dislocation gliding. Chemical composition along with processing ...
متن کامل