Modeling the Effect of Copper on Hardness of Microalloyed Dual Phase Steel through Neural Network and Neuro-fuzzy Systems

Abstract

The effects of copper along with some microalloying elements and the processing parameters are modeled with artificial neural network and adaptive neuro-fuzzy inference system. Both the tools are found to be useful for modeling the effect of copper and other alloying additions along with the processing parameters on the hardness of microalloyed DP steels. In case of the neural network, the proposed committee of models is found to be effective in handling the problem of mapping the input-output relation in these steels. The increase in the number of rules is found to improve the predictability of the neuro-fuzzy inference system. The predictions made by both the models substantiate the knowledge of physical metallurgy principles.