Neural network analysis of steel plate processing

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The process of rolling is very complicated and the number of parameters which determines the final properties can be quite large. It is extremely difficult therefore to develop a physical model for predicting various properties like yield and tensile strengths. In the present work, a neural network technique which can recognise complex relationships was employed to develop a quantitative method for estimating the yield and tensile strengths as a function of steel composition and rolling parameters. The model was tested extensively to confirm that the predictions are reasonable in the context of metallurgical principles and other data published in the literature. INTRODUCTION Cast steel is usually processed into usable products by severe plastic deformation, frequently using the rolling process. The purpose of this deformation is to refine the cast microstructure, to produce the steel in the required shape and to achieve the optimum mechanical properties. The properties depend not only...

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