Abstract
The vast compositional space of metallic materials provides ample opportunity to design stronger, more ductile and cheaper alloys. However, the substantial complexity of deformation micro-mechanisms makes simulation-based prediction of microstructural performance exceedingly difficult. In absence of predictive tools, tedious experiments have to be conducted to screen properties. Here, we develop a purely empirical model to forecast microstructural performance in advance, bypassing these challenges. This is achieved by combining in situ deformation experiments with a novel methodology that utilizes n-point statistics and principle component analysis to extract key microstructural features. We demonstrate this approach by predicting crack nucleation in a complex dual-phase steel, achieving substantial predictive ability (84.8% of microstructures predicted to crack, actually crack), a substantial improvement upon the alternate simulation-based approaches. This significant accuracy illustrates the utility of this alternate approach and opens the door to a wide range of alloy design tools.
Highlights
The field of physical metallurgy involves the creation of high strength and high ductility alloys, by relating the spatial characteristics of metal microstructures to their macroscopic properties[1,2]
We demonstrate its predictive ability on crack nucleation in a complex duel-phase (DP) steel, an alloy that is used widely in the automotive industry with complex micro-deformation mechanisms[11]
Each data-point requires information both before and after deformation, a number of points on the microstructure were tracked so that smaller images corresponding to the same region, both before and after deformation, could be compared (Supplementary Fig. 3)
Summary
The vast compositional space of metallic materials provides ample opportunity to design stronger, more ductile and cheaper alloys. We solve the main limitation of this approach, i.e. the absence of the undeformed state image, by using the deformed microstructure image and relying on the fact that edge detection deletes crack information, rendering it indistinguishable from a grain boundary (Fig. 3c inset and Supplementary Note). This ensures that the crack information is not used to predict cracking. This approach opens the door to a range of new tools and approaches for analyzing and understanding metallic materials
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