Subscribe Now Subscribe Today
Research Article

Artificial Neural Networks for Microstructure Analysis of Rolling Process

I.M. Qureshi , A. Naveed , T.A. Cheema and A. Jalil
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

The object of this work is to be able to predict the changes, which occur in the microstructure of metal and alloys during thermomechanical process like rolling. At present reliable model exists for its determination. Optimization of such processes normally demands a combination of several experiments and expensive trials. The final microstructure is dependent on various parameters such as alloy composition of metal, working temperature, local compression rate etc. Determination of grain size of an image of microstructure is difficult to predict with traditional micro-mechanical models. Neural networks however, are ideally suited to such non-linear, multi-parameter problems. In the present work, an attempt has been made to investigate and develop suitable neural network architecture, implementing multi-layer error-backpropagation algorithm, which is appropriate for this metallurgical application. The project lies at the boundary of the practical industrial problems and academic information analysis theory.

Related Articles in ASCI
Search in Google Scholar
View Citation
Report Citation

  How to cite this article:

I.M. Qureshi , A. Naveed , T.A. Cheema and A. Jalil , 2003. Artificial Neural Networks for Microstructure Analysis of Rolling Process. Information Technology Journal, 2: 65-68.

DOI: 10.3923/itj.2003.65.68



1:  Altan, T., 1983. Material Forming Fundamentals and Applications(ASM Series in Metal Processing). ASM International, UK., pp: 353

2:  Nigrin, A., 1993. Neural Network for Pattern Recognition. Massachusetts Institute of Technology, Cambridge, London, UK

3:  Bishop, C.M., 1995. Neural Network for Pttern Recognition. Oxford University Press, Oxford

4:  Bailer-Jones, C.A.L. and D.J.C. MacKay, 1998. Static and dynamic modelling of materials forging. Aust. J. Intell. Inform. Proc. Syst., 5: 10-16.

5:  Coryn, A.L., T.J. Bailer, J.C.S. David and P.J. Withers, 1997. Pregiction of deformed and annealed microstructure using bayesian neural networks and gaussian processes. Proceedings of the Australasia Pacific Forum on Intelligent Processing and Manufacturing of Materials, July 1997, Cambridge, UK., pp: 913-919

6:  Furu, T., H.R. Shercliff, C.M. Sellars and M.F. Ashby, 1996. Physically based modeling of strength, microstruture and recrystallistion during thermomechanical processing of Al-Mg alloys. Mater. Sci. Forum, 217-222: 453-458.

7:  James, A., 1994. Freeman, Simulating Neural Networks. 7th Edn., Addison-Wesley Professional, New York, ISBN-13: 978-0201566291, pp: 352
Direct Link  |  

8:  Mackay, D.J.C., 1995. Probable networks and plausible predictions: A review of practical bayesian methodes for supervoised neural networks. Network Compotation Neural Syst., 6: 469-469.

9:  Metal Hand Book, 1995. Metallography and Microstructures. 9th Edn., American Society of Materials, USA

10:  Rumelhart, D.E., G.E. Hinton and R.J. Williams, 1986. Learning representations by back-propagating errors. Nature, 323: 533-536.
CrossRef  |  

©  2022 Science Alert. All Rights Reserved