Network Modeling Approach to Predict the Effect of the Reinforcement Content on the Hot Strength of Al-based Composites

Abstract

In this investigation, the empirical formula that is used to predict the strength of the composite material has been compared to the predictions of the strength of Al-based composites by the trained artificial neural network (ANN), and trained Neuro-fuzzy network. To achieve this purpose, four schemes of evaluation have been used. The results showed that the Neuro-fuzzy network is the best tool to predict the hot deformation behavior of Al-based composites with different reinforcement contents (5, 15, and 20%) of Al₂O₃ particles that have an average particle size of 25 mm at different deformation conditions, since the reinforcement content and the deformation conditions have a nonlinear complex effect on the flow stress.

Keywords

modeling artificial neural network (ANN)neuro-fuzzy metal matrix composite (MMC)reinforcement

Get full access to this article

View all access options for this article.

References

Hashin, Z. (1983). J. Appl. Mech., 50: 481-505 .

Torquato, S. (1991). Appl. Mech. Rev., 44: 37-76 .

Milton, G. W. (1981). Phys. Rev. Lett., 46: 542-545 .

Davis, L. C. (1991). Metall. Trans. A, 22A: 3065-3067 .

Jalham, Issam S. and Baker, T. N. (1999). The 3rd Jordanian Mechanical and Industrial Engineering Conference, University of Applied Siences, Amman, Jordan, 9-12 May, pp. 583-609 .

Jalham, Issam S. and Baker, T. N. (1999). The 3rd Jordanian Mechanical and Industrial Engineering Conference, University of Applied Siences, Amman, Jordan, 9-12 May, pp. 611-634 .

Davis, L. C. and Allison, J. E. (1993). Metall. Trans. A, 24A: 2487-2496 .

Davis, L. C. and Allison, J. E. (1995). ASME Winter Annual Meeting, 12-17 November, San Francisco, CA.

McQueen, H. J. , Sakaris, P. and Bowles, J. (1993). Chandra, T. and Dhinra, A.B. (eds), Proc. Conf. ‘Advanced Composites 1993’, pp. 1193-1198 , Warrendale, PA, TMS.

10.

Razaghian, A. , YU, D. and Chandra, T. (1993). Chandra, T. and Dhinra, A.B. (eds), Proc. Conf. ‘Advanced Composites 1993’, pp. 1067-1071 , Warrendale, PA, TMS.

11.

Yu, D. and Ghandra, T. (1993). Mater. Sci. Forum, 113-115: 563-568 .

12.

Yu, D. and Ghandra, T. (1993). Chandra, T. and Dhinra, A.B. (eds), Proc. Conf. ‘Advanced Composites 1993’, pp. 1073-1077 , Warrendale, PA, TMS.

13.

Alunni, A. , Cerri, E. , Evangelista, E. and Forcellese, A. (1993). Chandra, T. and Dhinra, A.B. (eds), Proc. Conf. ‘Advanced Composites 1993’, pp. 1079-1085 , Warrendale, PA, TMS.

14.

Jarry, P. , Lone, W. and Bounvaist, J. (1987). Proc. 6th Conf. on ‘Composite Materials’, pp. 350-361 , London, Elsevier Applied Science.

15.

Sarkar, J. and Surappa, M. K. (1993). Chandra, T. and Dhinra, A.B. (eds), Proc. Conf. ‘Advanced Composites 1993’, pp. 1051-1054 , Warrendale, PA, TMS.

16.

Sarkar, J. , Parsad, V. and Surappa, M. K. (1993). Proc. 9th Int. Conf. On ‘Composite Materials’, pp. 240-246 , Zaragoza, Spain, Woodhead Publishing.

17.

Mannan, S. K. , Kumar, K. S. and Whittenberger, J. D. (1990). Metall. Trans. A, 21A, pp. 2179-2188 .

18.

Besterci, M. , Garafa, M. , Slesar, M. and Jangg, G. (1993). Scripta Metallurgica et Materialia, 29: 193-197 .

19.

Hwwu, Y. J. , Pan, Y. T. and Lenard, J. G. (1996). Steel Res., 67: 59 .

20.

Kong, L. X. , Hodgson, P. D. and Collinson, D. C. (1998). ISIJ International, 38(10): 1121-1129 .

21.

Jalham, Issam S. and Barghash, M. (2001). Journal of Composite Science and Technology, 61(9): 1225-1231 .

22.

Powell, M.I. D. (1987). Review Paper , In: IMA Conf. on Algorithms for Approximation of Functions and Data, RMCSN, Shrivenham, 1985 & in the 12th Biennial Numerical Analysis Conf., Dundee, pp. 223-241 .

23.

Micchelli, C. A. (1986). Constructive Approximation, 2: 11-22 .

24.

Jalham, Issam S. (2003). Journal of Composite Science and Technology, 63: 63-67 .

25.

Bao, G ,. Hutchinson, J. W. and McMeeking, R. M. (1991). J. Mech. Mater., 12: 85-94 .

26.

Bryan Harris , (1986). Engineering Composite Materials, The Institute of Metals .

27.

McDanels, D.L., Signorelli, R.A. and Weeton, J.W. (1967). NASA TN D-4173.

28.

Jang, J.-S. R. (1993). ANFIS (Adaptive-Network-Based fuzzy Inference Systems) , IEEE Trans. Systems, Man & Cybernetics, 23: 665-685 .

29.

Halgamuge, S. K. and Glesner, M. (1994). Neural Networks in Designing Fuzzy Systems for Real Word Applications , Fuzzy Sets and Systems, 65: 1-12 .

30.

Tschichold-Gurman (1996). RuleNet - A New Knowledge-Based Artificial Neural Network Model with Aplication Examples in Robotics, PhD Thesis, ETH Zurich.