Print Email Facebook Twitter Microstructure–property relation and machine learning prediction of hole expansion capacity of high-strength steels Title Microstructure–property relation and machine learning prediction of hole expansion capacity of high-strength steels Author Li, W. (TU Delft Team Kevin Rossi; Material Innovation Institute (M2i)) Vittorietti, M. (TU Delft Statistics) Jongbloed, G. (TU Delft Delft Institute of Applied Mathematics) Sietsma, J. (TU Delft Team Kevin Rossi) Date 2021 Abstract Abstract: The relationship between microstructure features and mechanical properties plays an important role in the design of materials and improvement of properties. Hole expansion capacity plays a fundamental role in defining the formability of metal sheets. Due to the complexity of the experimental procedure of testing hole expansion capacity, where many influencing factors contribute to the resulting values, the relationship between microstructure features and hole expansion capacity and the complexity of this relation is not yet fully understood. In the present study, an experimental dataset containing the phase constituents of 55 microstructures as well as corresponding properties, such as hole expansion capacity and yield strength, is collected from the literature. Statistical analysis of these data is conducted with the focus on hole expansion capacity in relation to individual phases, combinations of phases and number of phases. In addition, different machine learning methods contribute to the prediction of hole expansion capacity based on both phase fractions and chemical content. Deep learning gives the best prediction accuracy of hole expansion capacity based on phase fractions and chemical composition. Meanwhile, the influence of different microstructure features on hole expansion capacity is revealed. Graphical abstract: [Figure not available: see fulltext.] To reference this document use: http://resolver.tudelft.nl/uuid:3b150cf7-e276-4ea7-9b1e-18ab9278212b DOI https://doi.org/10.1007/s10853-021-06496-8 ISSN 0022-2461 Source Journal of Materials Science, 56 (34), 19228-19243 Part of collection Institutional Repository Document type journal article Rights © 2021 W. Li, M. Vittorietti, G. Jongbloed, J. Sietsma Files PDF Li2021_Article_Microstruc ... lation.pdf 1006.87 KB Close viewer /islandora/object/uuid:3b150cf7-e276-4ea7-9b1e-18ab9278212b/datastream/OBJ/view