MIDAS: a methodological framework for high-speed high-energy diffraction microscopy data reduction. Part I:...

Area of Science:

• Crystallography
• Materials Science
• Computational Science

Background:

• Modern synchrotron sources generate large-scale diffraction datasets, posing significant computational challenges.
• Analyzing these datasets requires robust methods for handling complex, noisy, and overlapping diffraction patterns.

Purpose of the Study:

• To present a unified algorithmic framework for analyzing both near-field (morphological) and far-field (orientational and strain) high-energy diffraction microscopy (HEDM) data.
• To develop a comprehensive approach integrating diverse data streams for accurate microstructural and micromechanical analysis.

Main Methods:

• Developed a generalized model for detector distortion correction.
• Implemented robust algorithms for peak identification in noisy and overlapping patterns.
• Created an efficient indexing formalism using Friedel pair symmetry and a decoupled iterative refinement scheme.

Main Results:

• Achieved orientation accuracy of ~0.05° and position accuracy of ~10 µm on experimental Ti-7Al data.
• Demonstrated a 190× improvement in lattice parameter precision on synthetic data compared to conventional methods.
• Validated the framework using experimental and synthetic datasets, confirming its accuracy and efficiency.

Conclusions:

• The integrated framework offers a powerful and extensible solution for processing HEDM data.
• This methodology enables the transformation of raw diffraction images into actionable microstructural and micromechanical insights.
• The approach addresses fundamental challenges in computational crystallography for modern experimental techniques.