A new method to generate artificial microstructure of dual phase steel using Teacher-Learner Based Optimization

Abstract

The dual phase steels are widely used in the manufacturing and automobile industry. The micromechanical analysis of the dual phase steel using microstructure based representative volume elements is the effective methodology for the estimation of its macroscopic properties. The real microstructure of the dual phase steels obtained using different microscopic analysis methods depicts the two main constituents viz. martensite inclusion in the ferrite matrix. The distribution of martensite in ferrite matrix exhibits a number of control parameters to define its characteristics. Generation of the artificial microstructure of dual phase steel based on these controlling parameters is advantageous to get a-priori estimate of the macroscopic properties and behavior. In the present work, a model is proposed for predicting the artificial microstructure of dual phase steel. The volume fraction of martensite and connectivity of the martensite in the ferrite matrix are used as controlling parameters to generate the artificial microstructure using the Teacher-Learner Based Optimization algorithm. The model has effectively predicted the microstructure of the DP590 steel. The artificial microstructure is applied for getting the tensile flow curve of the material using the finite element method. The predicted tensile response of the material is in good agreement with the experimental observations for DP590 steel. The model can be effectively applied to predict the artificial microstructure and subsequent micromechanical analysis of the dual phase steels.