Development of Vickers hardness prediction models via microstructural analysis and machine learning

Swetlana, S and Khatavkar, N and Singh, AK (2020) Development of Vickers hardness prediction models via microstructural analysis and machine learning. In: Journal of Materials Science, 55 (33). pp. 15845-15856.

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Abstract

Superalloys are high-temperature materials with outstanding strength and resistance to corrosion. A prior knowledge about its hardness is essential for development of new superalloys for its applications in aeronautics and power industries. Determining the hardness of a material with experiments is usually a destructive process. In this study, using structural, compositional and processing condition parameters as descriptors, machine learning (ML) models are developed to predict Vickers hardness. We employed image processing tools, which extract structural descriptors such as volume fraction, area, perimeter and aspect ratio of the phases in the microstructures. Using the structural features in combination with elemental and processing information as features, a Gaussian process regression model for the prediction of Vickers hardness of superalloys is developed. The model gives an unprecedented accuracy with a minimum root mean square error of 0.15. The descriptors provide insights into structure�property relationships, which are important for designing superalloys with improved Vickers hardness. The proposed method for extracting features from microstructures and combining them with elemental and processing information can be extended to develop ML model for prediction of other mechanical properties of superalloys.

Item Type:	Journal Article
Publication:	Journal of Materials Science
Publisher:	Springer
Additional Information:	The copyright of this article belongs to Springer
Keywords:	Aspect ratio; Corrosion resistance; Forecasting; High temperature corrosion; High temperature effects; Image processing; Machine learning; Mean square error; Microstructure; Predictive analytics; Regression analysis; Superalloys, Destructive process; Gaussian process regression model; High temperature materials; Image processing tools; Microstructural analysis; Processing condition; Root mean square errors; Structural descriptors, Vickers hardness
Department/Centre:	Division of Chemical Sciences > Materials Research Centre
Date Deposited:	29 Sep 2020 08:42
Last Modified:	29 Sep 2020 08:42
URI:	http://eprints.iisc.ac.in/id/eprint/66544

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