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Microstructural damage can occur during metal forming, but how and where this happens vary with the local microstructure and strain path. Large-scale analysis of such damage mechanisms is particularly important in advanced steels with a heterogeneous phase distribution. In our previous work, we demonstrated that deep learning enables a mechanism-based, statistical analysis by classifying many individual damage sites. The aim of this work is to generalize this approach to different stress states, e.g., biaxial instead of uniaxial tension, without manually labeling a large new ground-truth dataset of further micrographs and to thereby assess the changes in damage behavior with respect to stress state. Data augmentation and regularization allow us to directly apply our approach to the new, biaxial loading case. Overall, the network performance could be greatly improved and an analysis of changes in damage behavior, here the martensite crack angle distribution, with stress state can now be performed.

The behaviour of many materials is strongly influenced by the mechanical properties of hard phases, present either from deliberate introduction for reinforcement or as deleterious precipitates. While it is, therefore, self-evident that these phases should be studied, the ability to do so—particularly their plasticity—is hindered by their small sizes and lack of bulk ductility at room temperature. Many researchers have, therefore, turned to small-scale testing in order to suppress brittle fracture and study the deformation mechanisms of complex crystal structures. To characterise the plasticity of a hard and potentially anisotropic crystal, several steps and different nanomechanical testing techniques are involved, in particular nanoindentation and microcompression. The mechanical data can only be interpreted based on imaging and orientation measurements by electron microscopy. Here, we provide a tutorial to guide the collection, analysis, and interpretation of data on plasticity in hard crystals. We provide code collated in our group to help new researchers to analyse their data efficiently from the start. As part of the tutorial, we show how the slip systems and deformation mechanisms in intermetallics such as the Fe7Mo6 μ-phase are discovered, where the large and complex crystal structure precludes determining a priori even the slip planes in these phases. By comparison with other works in the literature, we also aim to identify “best practises” for researchers throughout to aid in the application of the methods to other materials systems.

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