Prediction of the flow stress for 30 MnSi steel using evolutionary least squares support vector machine and mathematical models

Proceedings of the IEEE International Conference on Industrial Technology

Volumn 2005, Issue , 2005, Pages 963-968

  Chen, Ai Ling ^a   Wang, Mu Lan ^b   Liu, Kun ^b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b NANJING INSTITUTE OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPACTION; LEARNING SYSTEMS; LEAST SQUARES APPROXIMATIONS; MANGANESE ALLOYS; MATHEMATICAL MODELS; PARAMETER ESTIMATION;

EVOLUTIONARY LEAST SQUARES; OPTIMAL PARAMETERS; SUPPORT VECTOR MACHINES; THERMAL SIMULATORS;

PLASTIC FLOW;

EID: 33847324418     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIT.2005.1600775     Document Type: Conference Paper

References (16)

1. S. Serajzadeh and A. K. Taheri, "Prediction of flow stress at hot working condition," Mechanics Research Communications, 30 (2003) 87-93.
2. C.P. Hong and J.J. Park, "Design of pass schedule for austenite grain refinement in plate rolling of a plain carbon steel," Journal of Materials Processing Technology, Vol, 143-144, December 20, 2003, pp. 758-763.
3. Y.S. Gao and P. Zhang, "Prediction of flow stress of steel 50CrV4 with artificial neural network," Iron and Steel, 33 (1998) 27-30 (in Chinese).
4. I. Schindler and E. Hadasik, "A new model describing the hot stress-strain curves of HSLA steel at high deformation," Journal of Materials Processing Technology, 106 (2000) 131-135.
5. R.H. Wu, J.T. Liu, H.B. Chang, T.Y. Hsu and X.Y. Ruan, "Prediction of the flow stress of 0.4C-1.9Cr-1.5Mn-1.0Ni-0.2Mo steel during hot deformation," Journal of materials processing technology, 116(2001)211-218.
6. J.T Liu, H.B. Chang, T.Y. Hsu and X.Y. Ruan, "Prediction of the flow stress of high-speed steel during hot deformation using a BP artificial neural network," Journal of materials processing technology, 103(2000)200-205.
7. M.P. Phaniraj and A.K. Lahiri, "The applicability of neural network model to predict flow stress for carbon steels," Journal of Materials Processing Technology, 141 (2003) 219-227.
8. P. Li, K.M. Xue, Y. Lu and J.R. Tan, "Neural network prediction of flow stress of Ti-15-3alloy under hot compression," Journal of Materials Processing Technology, 148(2004)235-238.
9. C.Z. Cai, W.L. Wang, L.Z. Sun and Y.Z. Chen, "Protein function classification via support vector machine approach," Mathematical Biosciences, 185 (2003) 111-122.
10. K.S. Shin, T.S. Lee and H.J. Kim, "An application of support vector machines in bankruptcy prediction model," Expert System with Applications, 28 (2005) 127-135.
11. K.J. Kim, "Financial time series forecasting using support vector machines," Neurocomputing, 55 (2003) 307-319.
12. W. Huang, Y. Nakamori and S.Y. Wang, "Forecasting stock market movement direction with support vector machine," Computers & Operations Research, 32 (2005) 2513-2522.
13. J.A.K. Suykens and J. Vandewall, "Least squares support vector machine classifiers," Neural Processing Letters, 1999, 9(3): 293-300
14. J.A.K. Suykens, J. Vandewalle and B. De Moor, "Optimal control by least squares support vector machines," Neural Networks, 14 (2001) 23-35.
15. J. Kennedy and R. Eberhart, "Particle swarm optimization," Proceedings of IEEE International Conference On neural Networks, Perth, Australia, 1995, 1942-1948.
16. S. He and Q.H. Wu, "A particle swarm optimizer with passive congregation," BioSystems, 78 (2004) 135-147.