Network science approach to predict the yield strength of metals containing soft particles and pores

Abstract

Local fluctuations in particle size and spatial distributions can substantially affect the yield strength of metals containing soft particles and/or porosities. The phenomenon can be studied via computational homogenization techniques, which, however, can be highly computationally demanding when large representative volume elements are needed. A more efficient approach to model the plastic deformation that controls the yield strength of metals containing pores has recently been proposed by the author and co-workers. The key idea is to treat the material as a network rather than as a continuum. In fact, plastic deformation in porous metals occurs via the formation of shear bands connecting the pores, and it can thus be modelled as a time-evolving network where the nodes represent the pores and the links represent the shear bands. So far, the applicability of this new approach has only been tested against 2D synthetic microstructures. The present work takes it a step further by applying the approach to real spheroidal graphite iron. Based on strain data collected in-situ via digital volume correlation, it is shown that the new network approach can explain the spatial variations of the plastic deformation that arise from local variations of the particle distribution throughout a tensile specimen.