Laurent, C, Bohme, B, Mengoni, M et al. (3 more authors) (2016) Prediction of the mechanical response of canine humerus to three-point bending using subject-specific finite element modelling. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 230 (7). pp. 639-649. ISSN 0954-4119
Abstract
Subject-specific finite element models could improve decision making in canine long-bone fracture repair. However, it preliminary requires that finite element models predicting the mechanical response of canine long bone are proposed and validated. We present here a combined experimental–numerical approach to test the ability of subject-specific finite element models to predict the bending response of seven pairs of canine humeri directly from medical images. Our results show that bending stiffness and yield load are predicted with a mean absolute error of 10.1% (±5.2%) for the 14 samples. This study constitutes a basis for the forthcoming optimization of canine long-bone fracture repair.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © IMechE 2016. This is an author produced version of a paper published in Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Finite element modelling; subject-specific; canine bone material properties; bending test; canine humerus |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Medical and Biological Engineering (iMBE) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Jun 2016 14:28 |
Last Modified: | 16 Jan 2018 09:27 |
Published Version: | http://dx.doi.org/10.1177/0954411916644269 |
Status: | Published |
Publisher: | SAGE Publications |
Identification Number: | 10.1177/0954411916644269 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:99218 |