Fatigue-tensile characterization and development of an ANN model for low cycle fatigue life prediction of dual phase steel under the influence of prestraining

Dual phase (DP) steel sheets are widely used in the automobile sector because of their high strength and adequate formability, which is required for press-forming into the desired shape. Prestrain induced strength increasing phenomena is a popular method in automotive sector. A study has been done on DP grade 600 steel sheet measuring 1.8 mm thick subjected to various levels of prestraining. The selected sheet is subjected to uniaxial tensile prestrain and characterizations like tensile test, hardness, and low cycle fatigue experiments have been executed with as-received and pre-strained steel sheets, and strength has been correlated with the level of prestraining. These experiments are supplemented by microstructural images obtained through optical microscope, scanning electron microscope (SEM), tensile and fatigue life analysis, and post-failure fractographic examinations. Positive response of prestraining is observed while measuring the tensile, hardness, and low cycle fatigue life. Cyclic hardening-softening during cyclic loading, striations on the fatigue failed surface are some of noticeable observations of the conducted experiment. Artificial neural network (ANN) models are preferred for their non-linear, data-driven technique while dealing with highly stochastic and complex data. The developed ANN model has been used for the prediction of fatigue life of as received and prestrained DP steel.