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Automated and quantitative analysis of plastic strain localization via multi-modal data recombination

Abstract : A multi-modal data recombination method that enables the automated, quantitative and statistical assessment of strain localization as a function of the microstructure is presented. It consists of merging high resolution digital image correlation (HR-DIC) datasets collected in a scanning electron microscope (SEM), with crystallographic data obtained from electron back-scattered diffraction (EBSD). As the data is typically gathered over large areas (about 1 mm2), this method enables the quantitative assessment of plastic strain localization over hundreds to thousands of grains, yet with a spatial resolution of tens of nanometers. The data is treated in a hierarchical manner so that strain localization phenomena can be studied as a function of phases, texture and grain orientation. The use of discontinuity tolerant DIC codes, such as Heaviside DIC (H-DIC) in the present case, enables identification the active slip system associated with slip band discontinuities. Analyses conducted over thousands of bands in thousands of grains enable the quantitative assessment of fundamental plasticity laws. The capabilities of this method are shown through application to Ti-6Al-4V and Inconel 718 alloys.
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Submitted on : Tuesday, March 24, 2020 - 4:57:51 PM
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M.A. Charpagne, J.C. Stinville, P.G. Callahan, Damien Texier, Z. Chen, et al.. Automated and quantitative analysis of plastic strain localization via multi-modal data recombination. Materials Characterization, Elsevier, 2020, 163, pp.1-16/110245. ⟨10.1016/j.matchar.2020.110245⟩. ⟨hal-02505530⟩



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