Abstract
The present study aims to model the composition, process and properties of Cu plus Ti, B microalloyed low carbon steels by using the artificial neural network (ANN) technique. This tool is found to be useful for modelling the effect of copper and microalloying additions along with the process parameters on the tensile properties using the experimental results generated by the present investigators. In first part of the modelling exercise, ANN was employed for prediction of the tensile properties from the input dataset, comprising composition and hot rolling parameters. Subsequently, prestrain and aging parameters were included in the dataset to predict their effects on the tensile properties. The predictions emerged from the modelling allow critical assessment of the role of Ti, B and Cu in conformance with established metallurgical principles.
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