Mining the Correlations Between Optical Micrographs and Mechanical Properties of Cold-Rolled HSLA Steels Using Machine Learning Approaches
| Autor: | Sezen Yucel, Berkay Yucel, Surya R. Kalidindi, Lode Duprez, A. Ray |
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| Rok vydání: | 2020 |
| Předmět: |
Structural material
business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image segmentation Microstructure Machine learning computer.software_genre Industrial and Manufacturing Engineering Salient Metallic materials General Materials Science Artificial intelligence business Representation (mathematics) computer |
| Zdroj: | Integrating Materials and Manufacturing Innovation. 9:240-256 |
| ISSN: | 2193-9772 2193-9764 |
| DOI: | 10.1007/s40192-020-00183-3 |
| Popis: | This paper demonstrates the feasibility of extracting quantitative linkages between optical micrographs and mechanical properties of cold-rolled HSLA (high-strength low alloy) steels measured in standardized tension tests. These linkages were established by bringing together modern toolsets for (i) image segmentation, (ii) rigorous statistical quantification of segmented microstructures, (iii) low-dimensional representation of microstructure statistics, and (iv) building surrogate models using emergent machine learning approaches. A salient aspect of the overall approach presented in this paper is that the extracted linkages exhibited remarkable predictive accuracy while utilizing only three features identified objectively (i.e., unsupervised) in the proposed overall workflow. |
| Databáze: | OpenAIRE |
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