Plastic deformation in metals predominantly occurs through crystallographic slip, which requires identification to advance the understanding of mechanical behaviour and micromechanical deformation mechanisms. Current identification methods include (i) the use of the Schmid Factor (SF), (ii) matching of observed experimental slip traces to theoretical slip traces as determined by, e.g., EBSD and, (iii) calculation and matching of the ‘Relative Displacement Ratio’ (RDR) along a pre-determined slip trace, derived from SEM-DIC data. However, these methods require the presence of clear and straight slip traces in the strain field. To identify plasticity which involves, e.g., cross-slip, curved slip, and/or diffuse slip, a method is required that performs a one-step identification, locally, on the SEM-DIC displacement/strain field, i.e., without requiring an initial identification of slip trace lines in the strain map. This paper proposes a novel slip system identification framework, termed SSLIP (for Slip Systems based Local Identification of Plasticity), in which the measured displacement gradient fields (from Digital Image Correlation) are locally matched to the kinematics of one or multiple combined theoretical slip systems, based on the measured crystal orientations. To identify the amount of slip that conforms to the measured kinematics, an optimization problem is solved for every datapoint individually, resulting in a slip activity field for every considered slip system. The identification framework is demonstrated and validated on an HCP virtual experiment, for discrete and diffuse slip, incorporating 24 slip systems. Experimental case studies on FCC and BCC metals show how full-field identification of discrete slip, diffuse slip and cross-slip becomes feasible, even when considering 48 slip systems for BCC. In summary, we will show at Euromat 2023 how the SSLIP method can be very useful for the materials science community, especially since we provide the codes in open source format on Github [1,2].

[1] Vermeij, T., Peerlings, R. H. J., Geers, M. G. D., & Hoefnagels, J. P. M. (2023). Automated identification of slip system activity fields from digital image correlation data. Acta Materialia, 243, 118502.

[2] www.github.com/TijmenVermeij/SSLIP