Deterministic segmentation of grains in dense Laue microdiffraction datasets

Dataset for the paper “Deterministic segmentation of grains in dense Laue microdiffraction datasets“

Sergio Bongiorno1, Jean-Sébastien Micha2, Samuel Tardif1 and Joël Eymery1 

1 Univ. Grenoble Alpes, CEA, IRIG, MEM, NRX, 38000 Grenoble, France; 

2 Univ. Grenoble Alpes, CNRS, UMR SyMMES, CEA, IRIG, 38000 Grenoble, France.

Synopsis. This work introduces a deterministic methodology for segmenting individual grain contributions from complex, high-density Laue microdiffraction datasets by leveraging spatial correlations and clustering of peak appearance maps.

Abstract.   Although Laue microdiffraction is an essential non-destructive technique for probing crystallographic orientation and strain at submicrometer resolution, the extreme density of overlapping Bragg reflections often hinders its application to polycrystalline materials. We present a novel analysis pipeline, implemented in the open-source Python package graintools, designed to segment these complex datasets without prior knowledge of the microstructure. The workflow uses a multi-round peak search to handle diverse spot morphologies and constructs a Look-Up Table to identify recurring reflections across raster-scan positions. These reflections are organized into spot families and represented as 2D binary appearance maps, which indicate the spatial occurrence of specific grains. To enhance data fidelity, Connected Component Analysis is applied to clean these maps before they are grouped into clusters using cosine similarity. The method was successfully validated on a 3C-SiC polycrystalline sample, demonstrating its ability to isolate grain footprints, extract accurate orientations, and reconstruct intra-grain deviatoric strain maps. This deterministic approach effectively circumvents the combinatorial and memory bottlenecks of traditional indexing and dictionary-based methods, providing a robust solution for analyzing highly populated diffraction patterns.

Details. The attached files contain the notebooks used to implement the pipeline described in the paper “Deterministic segmentation of grains in dense Laue microdiffraction datasets” and relative outputs.

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