Neural Network Analysis of Steel Plate

The process of rolling is very complicated and the number of parameters which determines the nal properties can be quite large. It is extremely diicult 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 connrm that the predictions are reasonable in the context of metallurgical principles and other data published in the literature.