Prediction of martensite formation start temperature in steels using artificial neural networks

被引：3
|
作者：
Arjomandi, M. [1 ]
Khorsand, H. [1 ]
Sadati, S. H. [1 ]
Abdoos, H. [1 ]
机构：
[1] KN Toosi Univ Technol, Dept Mech Engn, Mat Sci & Engn Grp, Tehran, Iran
来源：
DIFFUSION IN SOLIDS AND LIQUIDS III | 2008年 / 273-276卷
关键词：
martensite; BP neural network; steel; martensite start temperature Ms; KINETICS; NUCLEATION;
D O I：
10.4028/www.scientific.net/DDF.273-276.329
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Martensite phase and its formation are quite attractive and important in industrial Steels for reasons of having good properties such as high strength and high hardness. As such, determining the martensite formation start temperature in steel heat treatment operations is extremely important. Some parameters including chemical composition and grain size are effective factors on this temperature. In this investigation, we have made an attempt to determine this temperature with regard to chemical composition of steels. To reach this goal, we have explored the use of feedforward Artificial Neural Network (ANN) with the Back Propagation (BP) learning algorithm. A comparison is made between the Ms temperatures predicted with this model and those from the empirical equation as well as the experimental values obtained from costly and time-consuming tests in scientific and industrial centers for various steels. This comparison indicates that a better agreement exists between the ANN-predicted results and experimental values than the results from the empirical equation and experimental values.
引用
收藏
页码：329 / 334
页数：6
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