Prediction of Transition Temperature of Alloy steel Austenite Based on Self-Organizing Neural Network

2019 International Conference on Advanced Electronic Materials, Computers and Materials Engineering

无线电电子学;计算机科学;材料科学

Zhao, Wenya^1 ; Zhang, Yinguo^2

School of Chongqing Aerospace PolyTechnic, Chongqing, China^1

School of Chongqing University of Posts and Telecommunications, Chongqing, China^2

关键词: Austenite transformations;    Critical temperatures;    Heating temperatures;    Kohonen neural networks;    Physical experiments;    Prediction accuracy;    Self-organizing neural network;    Stepwise regression;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/563/2/022042/pdf
DOI  :  10.1088/1757-899X/563/2/022042

来源: IOP

【 摘 要 】

In order to determine the key chemical elements of the austenite transformation temperature (Ac1/Ac3) of alloy steel, this paper uses stepwise regression so that the number of neurons in the input layer of Kohonen self-organizing neural network is also determined and the network structure will be optimized. The learning algorithm is improved by combining non-tutor learning and supervised learning. Based on the optimized Kohonen neural network, an Ac1/Ac3 transition temperature predictive model was established. The prediction accuracy of this model which is based on Kohonen self-organizing neural network is higher in tests.The relative error of Ac1 is less than 3.01%, and the relative error of Ac3 is also below3.02%, which is significantly better than the prediction accuracy of stepwise regression. With predicting the critical temperature of Ac1/Ac3 based on Kohonen Network, the actual heating temperature can be determined, which has important practical application value in both steel heat treatment to guarantee quality and shortening the physical experiment period. CLC number: TP391 Document code: A.