Using Neural Networks to Predict the Hardness of Aluminum Alloys

  • B. Zahran Department of Computer Engineering, Al-Balqa Applied University, Jordan
Volume: 5 | Issue: 1 | Pages: 757-759 | February 2015 | https://doi.org/10.48084/etasr.529

Abstract

Aluminum alloys have gained significant industrial importance being involved in many of the light and heavy industries and especially in aerospace engineering. The mechanical properties of aluminum alloys are defined by a number of principal microstructural features. Conventional mathematical models of these properties are sometimes very complex to be analytically calculated. In this paper, a neural network model is used to predict the correlations between the hardness of aluminum alloys in relation to certain alloying elements. A backpropagation neural network is trained using a thorough dataset. The impact of certain elements is documented and an optimum structure is proposed

Keywords: aluminum alloys, hardness, neural networks

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References

D. G. Altenpohl, Aluminum: Technology, Applications and Environment: A Profile of a Modern Metal Aluminum from Within, 6th Edition, Wiley, 2010

R. S. Rana, R. Purohit, S. Das, “Reviews on the Influences of Alloying elements on the Microstructure and Mechanical Properties of Aluminum alloys and aluminum alloy composites”, International Journal of Scientific and Research Publications, Vol. 2, No. 6, pp. 1-7, 2012

L. Mondolfo, Aluminum alloys: structure and properties, Butterworths, 1976 DOI: https://doi.org/10.1016/B978-0-408-70932-3.50008-5

T. M. Mitchell, Machine Learning, McGraw-Hill, 1997

D. W. Patterson, Artificial Neural networks: Theory and Application, Prentice Hall, 1996

E.Alibeiki, J. Rajabi, J. Rajabi, “Prediction of mechanical properties of to heat treatment by Artificial Neural Networks”, Journal of Asian Scientific Research, Vol. 2, No. 11, pp. 742-746, 2012

S. K. Das, S. Kumari, “A Multi-Input Multi-Output Neural Network Model To Characterize Mechanical Properties Of Strip Rolled High Strength Low Alloy (HSLA) Steel”, MS’10 Prague Proceedings of the International Conference on Modelling and Simulation, Prague, Czech Republic, June 22–25, 2010

X. Liujie, X. Jiandong , W. Shizhong, Z. Songmin, Z. Yongzhen, L. Rui, “Use of Artificial Neural Network in Predicting Mechanical Properties of High-Speed Steel (HSS)”, Proceedings of the 2006 IEEE International Conference on Mechatronics and Automation, pp. 1872–1877, Luoyang, Henan, China, 2006 DOI: https://doi.org/10.1109/ICMA.2006.257520

F. Musharavati, A. S. M. Hamouda, “Application of artificial neural networks for modelling correlations in age hardenable Aluminum alloys”, Journal of Achievements in Materials and Manufacturing Engineering, Vol. 41, No. 1-2, pp. 140-146, 2010

J. H. Su, Q. M. Dong, P. Liu, H. J. Li, B. X. Kang, “Simulation of aging process of lead frame copper alloy by an artificial neural network”, Transactions of Nonferrous Metals Society Of China, Vol.13, No. 6, pp. 1419-1423, 2003

A. Kermanpur, A. Ebnonnasir, A. R. K. Yeganeh, J. Izadi, “Artificial Neural Network Modeling of High Pressure Descaling Operation in Hot Strip Rolling of Steels”, ISIJ International, Vol. 48, No. 7, pp. 963-970, 2008 DOI: https://doi.org/10.2355/isijinternational.48.963

M. B. Esfahani, M. R. Toroghinejad, S. Abbasi, “Artificial Neural Network Modeling the Tensile Strength of Hot Strip Mill Products”, ISIJ International, Vol. 49, No. 10, pp. 1583-1587, 2009 DOI: https://doi.org/10.2355/isijinternational.49.1583

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