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In order to improve the understanding of damage evolution in mechanicallyheterogeneous microstructures, like the ones of dual-phase steels, theinfluence of the applied stress state is a key element. In this work, westudied the influence of the globally applied stress state on the evolution ofdamage in such a microstructure. Classical damage models allow predictions ofdamage during deformation based on considerations of the material as anisotropic continuum. Here, we investigate their validity in a dual phasemicrostructure that is locally dominated by its microstructural morphologicalcomplexity based on a statistical ensemble of thousands of individual voidsformed under different stress states. For this purpose, we combined acalibrated material model incorporating damage formation to assess the localstress state in samples with different notch geometries and high-resolutionelectron microscopy of large areas using a deep learning-based automatedmicrograph analysis to detect and classify microstructural voids according totheir source of origin. This allowed us to obtain both the continuum stressstate during deformation and statistically relevant data of individual voidformation. We found that the applied plastic strain is the major influence onthe overall number, and therefore the nucleation of new voids, whiletriaxiality correlates with the median void size, supporting its proposedinfluence on void growth. In contrast, coalescence of voids leading to failureappears related to local instabilities in the form of shear band formation andis therefore only indirectly determined by the global stress state in that itdetermines the global distribution, density and size of voids.

On the effect of strain and triaxiality on void evolution in a  heterogeneous microstructure -- A statistical and single void study of damage  in DP800