Quantitative Characterization of Ductility for Fractographic Analysis - Archive ouverte HAL Access content directly
Conference Papers Year : 2022

Quantitative Characterization of Ductility for Fractographic Analysis

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We develop a machine-learning image segmentation pipeline that detects ductile (as opposed to brittle) fracture in fractography images. To demonstrate the validity of our approach, use is made of a set of fractography images representing fracture surfaces from cold-spray deposits. The coatings have been subjected to varying heat treatments in an effort to improve their mechanical properties. These treatments yield markedly different microstructures and result in a wide range of mechanical properties that combine brittle and ductile fracture once the materials undergo rupture. To detect regions of ductile fracture, we propose a simple machine learning network based on a 32-layers U-Net framework and trained on a set of small image patches. These regions most often contain small dimples and differ by the surface roughness. Overall, the machine-learning method shows good predictive capabilities when compared to segmentation by a human expert. Finally, we highlight other possible applications and improvements of the proposed method.
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hal-03886886 , version 1 (06-12-2022)



Laury-Hann Brassart, Samy Blusseau, François Willot, F. Delloro, Gilles Rolland, et al.. Quantitative Characterization of Ductility for Fractographic Analysis. ECMI: European Consortium for Mathematics in Industry 2021, ECMI, Apr 2021, Wupperthal, Germany. pp.349-355, ⟨10.1007/978-3-031-11818-0_46⟩. ⟨hal-03886886⟩
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