Prediction of microstructural representativity from a single image
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866915411932479488 |
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| author | Dahari, Amir Docherty, Ronan Kench, Steve Cooper, Samuel J. |
| author_facet | Dahari, Amir Docherty, Ronan Kench, Steve Cooper, Samuel J. |
| contents | In this study, we present a method for predicting the representativity of the phase fraction observed in a single image (2D or 3D) of a material. Traditional approaches often require large datasets and extensive statistical analysis to estimate the Integral Range, a key factor in determining the variance of microstructural properties. Our method leverages the Two-Point Correlation function to directly estimate the variance from a single image, thereby enabling phase fraction prediction with associated confidence levels. We validate our approach using open-source datasets, demonstrating its efficacy across diverse microstructures. This technique significantly reduces the data requirements for representativity analysis, providing a practical tool for material scientists and engineers working with limited microstructural data. To make the method easily accessible, we have created a web-application, www.imagerep.io, for quick, simple and informative use of the method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19568 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prediction of microstructural representativity from a single image Dahari, Amir Docherty, Ronan Kench, Steve Cooper, Samuel J. Computation Computer Vision and Pattern Recognition Applications In this study, we present a method for predicting the representativity of the phase fraction observed in a single image (2D or 3D) of a material. Traditional approaches often require large datasets and extensive statistical analysis to estimate the Integral Range, a key factor in determining the variance of microstructural properties. Our method leverages the Two-Point Correlation function to directly estimate the variance from a single image, thereby enabling phase fraction prediction with associated confidence levels. We validate our approach using open-source datasets, demonstrating its efficacy across diverse microstructures. This technique significantly reduces the data requirements for representativity analysis, providing a practical tool for material scientists and engineers working with limited microstructural data. To make the method easily accessible, we have created a web-application, www.imagerep.io, for quick, simple and informative use of the method. |
| title | Prediction of microstructural representativity from a single image |
| topic | Computation Computer Vision and Pattern Recognition Applications |
| url | https://arxiv.org/abs/2410.19568 |