Design of medium carbon steels by computational intelligence techniques

• Medium carbon steels were designed by computational intelligence techniques.
• Neural networks model identified the important input parameters.
• Neural network model has been used as fitness function for genetic algorithms.
• The model provided a meaningful combination of inputs for the desired properties.

Steel design with the targeted properties is a challenging task due to the involvement of many variables and their complex interactions. Artificial neural networks (ANN) recognized for representing the complex relationships and genetic algorithms (GA) are successful for optimization of many real world problems. ANN has been used to identify the relative importance of variables those control the mechanical properties of medium carbon steels. We propose the combination of ANN and GA to optimize composition and heat treatment parameters for the desired mechanical properties. The trained ANN model was used as a fitness function and also as a predictive model. The predicted properties were realistic and higher for the model suggested with the optimum combination of composition and heat treatment variables. The proposed framework is expected to be useful in reducing the experiments required for designing new steels.

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