Abstract

In order to improve the understanding of damage evolution in mechanically heterogeneous microstructures, like the ones of dual-phase steels, the influence of the applied stress state is a key element. In this work, we studied the influence of the globally applied stress state on the evolution of damage in such a microstructure. Classical damage models allow predictions of damage during deformation based on considerations of the material as an isotropic continuum. Here, we investigate their validity in a dual phase microstructure that is locally dominated by its microstructural morphological complexity based on a statistical ensemble of thousands of individual voids formed under different stress states. For this purpose, we combined a calibrated material model incorporating damage formation to assess the local stress state in samples with different notch geometries and high-resolution electron microscopy of large areas using a deep learning-based automated micrograph analysis to detect and classify microstructural voids according to their source of origin. This allowed us to obtain both the continuum stress state during deformation and statistically relevant data of individual void formation. We found that the applied plastic strain is the major influence on the overall number, and therefore the nucleation of new voids, while triaxiality correlates with the median void size, supporting its proposed influence on void growth. In contrast, coalescence of voids leading to failure appears related to local instabilities in the form of shear band formation and is therefore only indirectly determined by the global stress state in that it determines the global distribution, density and size of voids.

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