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	Materialometrical approach of predicting the austenite formation temperatures
	You, W ; Xu, WH ; Bai, BZ ; Fang, HS
	2010-05-10 ; 2010-05-10
关键词	austenite formation ternperatures-Ac 1, Ac3 artificial neural network performance of prediction alloying elements quantitative effects MULTILAYER FEEDFORWARD NETWORKS NEURAL-NETWORKS TRANSFORMATIONS Nanoscience & Nanotechnology Materials Science, Multidisciplinary
中文摘要	Artificial neural network model-one of materialometrical approaches was developed basing on experimental data collected from domestic and foreign literatures to predict the austenite formation temperatures (Ac3 and Ac1) of steels. Scatters diagrams and statistical criteria showed that the prediction performance of artificial neural network is superior to that of Andrews formulae. Moreover, the quantitative effects of alloying elements on Ac3 and Ac1 temperatures were analysed using neural network models, the results showed that there exists nonlinear relationship between contents of alloying elements and the Ac3 and Ac1 temperatures which is mainly related to the interaction among the alloying elements in steels. (c) 2006 Elsevier B.V. All rights reserved.
语种	英语 ; 英语
出版者	ELSEVIER SCIENCE SA ; LAUSANNE ; PO BOX 564, 1001 LAUSANNE, SWITZERLAND
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/20704]
专题	清华大学
推荐引用方式 GB/T 7714	You, W,Xu, WH,Bai, BZ,et al. Materialometrical approach of predicting the austenite formation temperatures[J],2010, 2010.
APA	You, W,Xu, WH,Bai, BZ,&Fang, HS.(2010).Materialometrical approach of predicting the austenite formation temperatures..
MLA	You, W,et al."Materialometrical approach of predicting the austenite formation temperatures".(2010).

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