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Microstructural damage sensitivity prediction using spatial statistics.

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.

SUBMITTER: Cameron BC

PROVIDER: S-EPMC6391476 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Microstructural damage sensitivity prediction using spatial statistics.

Cameron B C BC Tasan C C CC

Scientific reports 20190226 1

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 challeng ...[more]

PMID: 30808884

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Project description:ObjectiveSpinocerebellar ataxia type 3 (SCA3) is the most commonly occurring type of autosomal dominant spinocerebellar ataxia. The present study aims to investigate progressive changes in white matter (WM) fiber in asymptomatic and symptomatic patients with SCA3.MethodsA total of 62 participants were included in this study. Among them, 16 were asymptomatic mutation carriers (pre-SCA3), 22 were SCA3 patients with clinical symptoms, and 24 were normal controls (NC). Group comparison of tract-based spatial statistics was performed to identify microstructural abnormalities at different SCA3 disease stages.ResultsDecreased fractional anisotropy (FA) and increased mean diffusivity (MD) were found in the left inferior cerebellar peduncle and superior cerebellar peduncle (SCP) in the pre-SCA3 group compared with NC. The symptomatic SCA3 group showed brain-wide WM tracts impairment in both supratentorial and infratentorial networks, and the mean FA value of the WM skeleton showed a significantly negative correlation with the International Cooperative Ataxia Rating Scale (ICARS) scores. Specifically, FA of the bilateral posterior limb of the internal capsule negatively correlated with SCA3 disease duration. We also found that FA values in the right medial lemniscus and SCP negatively correlated with ICARS scores, whereas FA in the right posterior thalamic radiation positively correlated with Montreal Cognitive Assessment scores. In addition, MD in the middle cerebellar peduncle, left anterior limb of internal capsule, external capsule, and superior corona radiate positively correlated with ICARS scores in SCA3 patients.ConclusionWM microstructural changes are present even in the asymptomatic stages of SCA3. In individuals in which the disease has progressed to the symptomatic stage, the integrity of WM fibers across the whole brain is affected. Furthermore, abnormalities in WM tracts are closely related to SCA3 disease severity, including movement disorder and cognitive dysfunction. These findings can deepen our understanding of the neural basis of SCA3 dysfunction.

Dataset Information

Microstructural damage sensitivity prediction using spatial statistics.

Publications

Microstructural damage sensitivity prediction using spatial statistics.

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