Gitman, I.M., Gitman, M.B., Stolbov, V.Y. et al. (2 more authors) (2019) Methodology to estimate the minimum number of experiments and key microstructural parameters in macroscopic strength properties evaluation. ZAMM, 99 (3). e201800259. ISSN 0044-2267
Abstract
A novel methodology, based on the theory of fuzzy sets, to obtain materials with pre‐defined sets of strength properties has been analysed from the position of identifying the necessary and sufficient number of experiments needed to predict these macro characteristics and establishing which micro parameters significantly influence the macroscale results. The procedure to estimate, with a user‐defined degree of accuracy, the minimum number of experiments and significant micro parameters has been tested and verified using experimental data, obtained from digital images of material microsections under different heat treatment conditions while analysing strength properties of reinforcing steel. The results confirm the possibility of using the developed methodologies for the performance properties evaluation of materials based on the minimum number of experiments and identification of the key grain‐phase parameters.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim. This is an author produced version of a paper subsequently published in ZAMM. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Fuzzy sets; microstructure parameters; strength properties |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) |
Funding Information: | Funder Grant number European Commission H2020-MSCA-RISE-2016 734485 EUROPEAN COMMISSION - HORIZON 2020 734485 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Feb 2019 13:42 |
Last Modified: | 17 Nov 2021 09:02 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1002/zamm.201800259 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:142000 |