Artificial Neural Network-Based Modeling for Impact Energy of Cast Duplex Stainless Steel

The exploitation of artificial neural network as a computational technique in predicting the impact energy of cast duplex stainless steels based on its chemical composition is reported in this research work. Two hundred and twenty melts of duplex stainless steel of different compositions were casted, heat-treated and tested for Charpy impact test. A multilayer feed forward ANN model was developed based on 75% of the available chemical compositions of duplex stainless steel as input and impact energy in joules as output. The prediction efficiency of the developed models was calculated based on mean absolute error and mean absolute percentage error; the best model thus sorted out was validated and tested. A multilayer feed forward ANN model with two hidden layers was selected which provided better linear correlation between the chemical composition and impact energy. Correlation performance of considered ANN model with network topology expressed in terms of mean absolute percent error was found to be 0.43% with a correlation coefficient value of 0.95714. Testing and evaluation of the developed model proved to be efficient enough for the development of duplex stainless steels with required impact toughness.