A Data Analytics Approach to Discovering Unique Microstructural Configurations Susceptible to Fatigue
| Autor: | J. M. Larsen, W.J. Porter, V. Sinha, R. M. Hoffman, Reji John, Sushant K. Jha, Dennis J. Buchanan, R. A. Brockman, Adam L. Pilchak |
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| Rok vydání: | 2018 |
| Předmět: |
010302 applied physics
General Engineering Fatigue testing 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Fuzzy logic Condensed Matter::Materials Science 0103 physical sciences Principal component analysis Metric (mathematics) Data analysis Cluster (physics) General Materials Science Point (geometry) 0210 nano-technology Biological system Cluster analysis Mathematics |
| Zdroj: | JOM. 70:1147-1153 |
| ISSN: | 1543-1851 1047-4838 |
| DOI: | 10.1007/s11837-018-2881-5 |
| Popis: | Principal component analysis and fuzzy c-means clustering algorithms were applied to slip-induced strain and geometric metric data in an attempt to discover unique microstructural configurations and their frequencies of occurrence in statistically representative instantiations of a titanium alloy microstructure. Grain-averaged fatigue indicator parameters were calculated for the same instantiation. The fatigue indicator parameters strongly correlated with the spatial location of the microstructural configurations in the principal components space. The fuzzy c-means clustering method identified clusters of data that varied in terms of their average fatigue indicator parameters. Furthermore, the number of points in each cluster was inversely correlated to the average fatigue indicator parameter. This analysis demonstrates that data-driven methods have significant potential for providing unbiased determination of unique microstructural configurations and their frequencies of occurrence in a given volume from the point of view of strain localization and fatigue crack initiation. |
| Databáze: | OpenAIRE |
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