Intelligent process modelling using Feed-Forward Neural Networks

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

International Journal of Manufacturing Technology and Management, 2010, vol. 19, issue 3/4, 238-257

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. (search for similar items in EconPapers)
Date: 2010
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