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Genetic programming for the prediction of tensile strength of cast iron

Duda J., Stawowy A.

Archives of Foundry Engineering

2011

Vol. 11, iss. 4

31-34

In this paper we propose genetic programming (GP) to predict tensile strength of ductile cast iron. The chemical composition and pouring temperature were used as explanatory input variables (parameters), while tensile strength as dependent output variable (response). On the basis of real data set collected in a one of the Polish foundries, two different models for output variable were developed by genetic programming. Statistical analysis of obtained results and two test cases were employed to compare the accuracy of the GP model with the neural network (NN) model and a linear multiple regression model. The comparison demonstrated that the GP outperforms regress ion techniques, while it is generally worse than NN. Nevertheless GP can be a powerful tool for predicting the mechanical properties of cast iron as it provides a mathematical model, which can be further analyzed.