Title: Intelligent process modelling using Feed-Forward Neural Networks

Authors: Mohamed H. Gadallah, Khaled Abdel Hamid El-Sayed, Keith Hekman

Addresses: Department of Operations Research, Institute of Statistical Studies and Research, Cairo University, 5 Tharwat Street, Orman, Dokki, Giza, 12613, Egypt. ' Department of Mechanical Engineering, The American University in Cairo, Egypt. ' Department of Mechanical Engineering, The American University in Cairo, Egypt

Abstract: A supervised Feed-Forward Neural Network (FFNN) is developed. Since Neural Networks (NN) are expensive techniques, Design of Experiments and statistical techniques are employed to offset this expense. Sometimes information is not available, in such a case, the modeller can compromise accuracy for the experimental cost. Results show that each model has an approximation capability. One or more models, once added results in enhanced modelling capacity. Different models are developed and their convergence are investigated. Conclusions indicate that neural networks are valid modelling techniques. Cost of developed models is high and can be offset with approximation tools such as design of experiments.

Keywords: FFNN; feed-forward neural networks; intelligent modelling; process modelling; simulation; design of experiments; DOE; orthogonal arrays; flat end milling.

DOI: 10.1504/IJMTM.2010.031371

International Journal of Manufacturing Technology and Management, 2010 Vol.19 No.3/4, pp.238 - 257

Published online: 02 Feb 2010 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article