Data clustering for the high-resolution alignment of microstructure and strain fields

The emergence of small-scale deformation mapping by a combination of scanning electron microscopy and digital image correlation (SEM-DIC) has enabled full-field investigations into the complex roles of microstructure on microscale deformation mechanisms. However, these investigations are hindered by errors in alignment between the microstructure data, generally acquired by electron backscatter diffraction (EBSD), and the microscale strain data obtained by SEM-DIC. Distortions, stitching artifacts, and spatial resolution differences between microstructure and strain fields can lead to misalignments on the order of μms. This alignment uncertainty introduces error into microstructure-strain localization analyses and precludes the examination of deformation mechanisms near grain boundaries. To improve alignment between EBSD-obtained grain boundaries and SEM-DIC strain data, an approach was created wherein a mantle was first established around the EBSD-acquired grain boundaries. Strain data was then clustered within this mantle using a k-means algorithm to identify grain boundary strain localization, and these boundary points were fit to obtain a continuous curve for each individual boundary. Clustered point outliers, such as those due to grain boundary-local dislocation slip, were statistically identified by using an absolute error threshold and removed from the grain boundary fit. The resulting identification of grain boundaries in the microscale strain data is significantly improved from EBSD-identified boundaries. •Precise alignment of microstructure and strain fields is difficult and limits high-resolution characterization of grain boundary deformation behavior.•K-means clustering within strain space to identify strain localization at grain boundaries, within a mantle defined by EBSD-identified boundaries.•Clustered strain data is fit, using an iterative fit refinement process, to remove clustered point noise and obtain continuous grain boundary curves.•The clustering and fitting method produced boundary curves with higher spatial resolution and corrected boundary alignment errors up to 20 μm.