Analysis of the effects of processing parameters on mechanical properties and formability of cold rolled low carbon steel sheets using neural networks
作者： H. Monajati , D. Asefi , A. Parsapour , Sh. Abbasi
DOI: 10.1016/J.COMMATSCI.2010.06.040
关键词:
摘要: Abstract In the present study, an artificial neural network (ANN) is used to describe effects of processing parameters on evolution mechanical properties and formability deep drawing quality (DDQ) steel sheets. This model a feed forward back-propagation (BPNN) with set 19 including chemical composition, hot cold rolling parameters, subsequent batch annealing process predict final properties, yield strength (YS), work hardening exponent (n), plastic strain ratio ( r ¯ ), ANN system was trained using prepared training set. After process, test data were check accuracy. The results show that can be as quantitative guide control commercial low carbon products.
elsevier.com
sci-hub.se 参考文章(14)
Amit Kumar Gupta, D. Ravi Kumar, Formability of galvanized interstitial-free steel sheets Journal of Materials Processing Technology. ,vol. 172, pp. 225- 237 ,(2006) , 10.1016/J.JMATPROTEC.2005.10.016
M. A. Akoy, E. S. Kayali, H. Cimenoglu, The Influence of Microstructural Features and Mechanical Properties on the Cold Formability of Ferritic Steel Sheets Isij International. ,vol. 44, pp. 422- 428 ,(2004) , 10.2355/ISIJINTERNATIONAL.44.422
R. K. Ray, J. J. Jonas, R. E. Hook, Cold rolling and annealing textures in low carbon and extra low carbon steels International Materials Reviews. ,vol. 39, pp. 129- 172 ,(1994) , 10.1179/IMR.1994.39.4.129
T. De Cock, C. Capdevila, F.G. Caballero, C. García de Andrés, Global recrystallisation model of low carbon sheet steels with different cementite contents Materials Science and Engineering: A. ,vol. 519, pp. 9- 18 ,(2009) , 10.1016/J.MSEA.2009.04.045
S K Paul, Sanak Mishra, On the formability of sheet steels Bulletin of Materials Science. ,vol. 19, pp. 963- 984 ,(1996) , 10.1007/BF02744631
E. Kozeschnik, V. Pletenev, N. Zolotorevsky, B. Buchmayr, Aluminum nitride precipitation and texture development in batch-annealed bake-hardening steel Metallurgical and Materials Transactions A-physical Metallurgy and Materials Science. ,vol. 30, pp. 1663- 1673 ,(1999) , 10.1007/S11661-999-0104-Y
N.S. Reddy, J. Krishnaiah, Seong-Gu Hong, Jae Sang Lee, Modeling medium carbon steels by using artificial neural networks Materials Science and Engineering A-structural Materials Properties Microstructure and Processing. ,vol. 508, pp. 93- 105 ,(2009) , 10.1016/J.MSEA.2008.12.022
C. Garcia-Mateo, F.G. Caballero, C. García de Andrés, C. Capdevila, Neural network analysis of the influence of processing on strength and ductility of automotive low carbon sheet steels Computational Materials Science. ,vol. 38, pp. 192- 201 ,(2006) , 10.1016/J.COMMATSCI.2006.02.005
P. Antoine, S. Vandeputte, J.-B. Vogt, Empirical model predicting the value of the strain-hardening exponent of a Ti-IF steel grade Materials Science and Engineering A-structural Materials Properties Microstructure and Processing. ,vol. 433, pp. 55- 63 ,(2006) , 10.1016/J.MSEA.2006.06.030
K. Elangovan, C. Sathiya Narayanan, R. Narayanasamy, Modelling of forming limit diagram of perforated commercial pure aluminium sheets using artificial neural network Computational Materials Science. ,vol. 47, pp. 1072- 1078 ,(2010) , 10.1016/J.COMMATSCI.2009.12.016