Prediction of Mechanical Properties of Sensitized Stainless Steel by Neural Network Modeling and Validation Using Ball Indentation Test

Elevated temperature sensitization of a 304 stainless steel results in degradation of mechanical properties
and becomes prone to premature failure. In the present investigation, sensitization of 304 stainless steel has
been done in the temperature range of 500–800 C. Yield strength, ultimate tensile strength and fracture
toughness (KJc) of the sensitized 304 stainless steel specimens were determined by ball indentation technique. Microstructural characteristics were quantified and used in artificial neural network to predict the
mechanical properties of the investigated alloy. Neural network was developed with the help of MATLAB
toolbox. Best equation was fitted for training, testing and validating the output. Predicted values from the
developed model exhibited impressive correlation with experimental data obtained through ball indentation
technique as well as with literature reports. The model has proved its distinctive potential in predicting the
mechanical properties of sensitized 304 stainless steel, which faces restriction in bulk sampling from original
component to perform conventional mechanical test during service exposure.