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SR-CLD: Spatially Resolved Chord Length Distributions for Statistical Description and Visualization of Non-uniform Microstructures

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arxiv 2409.03729 v3 pith:WA7GUCC2 submitted 2024-09-05 cond-mat.mtrl-sci

classification cond-mat.mtrl-sci
keywords microstructuressr-cldnon-uniformdistributionscalculationchorddetailedefficient
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This study introduces the calculation of spatially-resolved chord length distribution (SR-CLD) as an efficient approach for quantifying and visualizing non-uniform microstructures in heterogeneous materials. SR-CLD enables detailed analysis of spatial variation of microstructures in different directions that can be overlooked with traditional descriptions. We present the calculation of SR-CLD using efficient scan-line algorithm that counts pixels in constituents along pixel rows or columns of microstructure images for detailed, high-resolution SR-CLD maps. We demonstrate the application of SR-CLD in three case studies: on synthetic polycrystalline microstructures with known and intentionally created uniform and gradient spatial distributions of grain size; on non-uniform microstructures from welding simulations; and on experimental images of two-phase microstructures of additively manufactured Ti alloys with significant spatially non-uniform distributions of laths of one of the phases.

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  1. Machine learning of microstructure--property relationships in materials leveraging microstructure representation from foundational vision transformers

    cs.CV 2025-01 conditional novelty 6.0 of 10

    Pre-trained vision transformers can extract microstructure features that support competitive machine-learning predictions of elastic modulus and Vickers hardness without fine-tuning.

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