Artificial Neural Networks Investigation of Indentation Force Effects on Nano- and Microhardness of Dual Phase Steels

Nanoindentation test results on different grain sizes of dual phase (DP) steels are used to train artificial neural networks (ANNs). With selection of ferrite and martensite grain size, martensite volume fraction (MVF), and indentation force as input and microhardness, ferrite, and martensite nanohardness as outputs, six different ANNs are trained according to normalized datasets to predict hardness and their tolerances. A graphical user interface (GUI) is developed for a better investigation of the trained ANN prediction. The response of the ANN is analyzed in five case studies. In each case the variation of two input parameters on the output is analyzed when the other input parameters are kept constant. Reliable and reasonable results of ANN predictions are achieved in each case

Mechanisms of void formation during tensile testing in a commercial, dual-phase steel

A detailed analysis of the microstructure and failure mechanism of a dual-phase steel material as a function of strain was conducted. Accordingly, three tensile tests were performed and interrupted at different strain levels in order to investigate void nucleation, void growth and void coalescence. Scanning electron microscopy analysis revealed that void nucleation occurs by ferrite grain-boundary decohesion in the neighborhood of martensite grains. Further, void initiation could be observed between closely situated inartensite grains. Martensite morphology and distribution has a significant impact on the accumulation of damage. The mechanism of failure was found to be influenced by deformation localization due to microstructural inhomogeneity. Based on the experimental observations and simulation results, a model describing the failure mechanism is proposed for dual-phase steel material