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Predicting implant UHMWPE wear in-silico: A robust, adaptable computational-numerical framework for future theoretical models

Predicting implant UHMWPE wear in-silico: A robust, adaptable computational-numerical framework for future theoretical models
Predicting implant UHMWPE wear in-silico: A robust, adaptable computational-numerical framework for future theoretical models
Computational methods for the pre-clinical wear prediction for devices such as hip, knee or spinal implants are valuable both to industry and academia. Archard’s wear model laid the basis for the first generation of theoretical wear estimation algorithms, and this has been adapted to account for the importance of multi-directional sliding. These second-generation cross-shear algorithms are useful, but they leave room for improvement.
In this paper, we outline a generalised framework for a ‘third generation’ wear model. The essential feature of this proposed approach is that it removes the acausality and scale-independence of current second-generation algorithms. The methodology is presented in such a way that any existing second-generation model could be adapted using this framework. Using this approach, the predictive power against pin-on-disc and implant tests is shown to be improved; however, the model is still essentially a purely adhesive-abrasive wear predictor, accounting for only a limited number of factors as part of the tribological process. Further ongoing work is needed to expand and improve upon the current capabilities of in-silico UHMWPE wear prediction capabilities.
bio-tribology, uhmwpe wear, computational modelling, wear theories
0043-1648
Strickland, M.A.
605405d5-e9e9-434e-a092-5a4565a34e9b
Dressler, M.R.
154f5835-f70e-463d-849a-3a83c46842c1
Taylor, M.
e368bda3-6ca5-4178-80e9-41a689badeeb
Strickland, M.A.
605405d5-e9e9-434e-a092-5a4565a34e9b
Dressler, M.R.
154f5835-f70e-463d-849a-3a83c46842c1
Taylor, M.
e368bda3-6ca5-4178-80e9-41a689badeeb

Strickland, M.A., Dressler, M.R. and Taylor, M. (2011) Predicting implant UHMWPE wear in-silico: A robust, adaptable computational-numerical framework for future theoretical models. Wear. (doi:10.1016/j.wear.2011.08.020).

Record type: Article

Abstract

Computational methods for the pre-clinical wear prediction for devices such as hip, knee or spinal implants are valuable both to industry and academia. Archard’s wear model laid the basis for the first generation of theoretical wear estimation algorithms, and this has been adapted to account for the importance of multi-directional sliding. These second-generation cross-shear algorithms are useful, but they leave room for improvement.
In this paper, we outline a generalised framework for a ‘third generation’ wear model. The essential feature of this proposed approach is that it removes the acausality and scale-independence of current second-generation algorithms. The methodology is presented in such a way that any existing second-generation model could be adapted using this framework. Using this approach, the predictive power against pin-on-disc and implant tests is shown to be improved; however, the model is still essentially a purely adhesive-abrasive wear predictor, accounting for only a limited number of factors as part of the tribological process. Further ongoing work is needed to expand and improve upon the current capabilities of in-silico UHMWPE wear prediction capabilities.

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Published date: 22 August 2011
Keywords: bio-tribology, uhmwpe wear, computational modelling, wear theories
Organisations: Faculty of Engineering and the Environment

Identifiers

Local EPrints ID: 197277
URI: http://eprints.soton.ac.uk/id/eprint/197277
ISSN: 0043-1648
PURE UUID: af435360-5f51-410e-845e-fc44170b603f

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Date deposited: 21 Sep 2011 08:54
Last modified: 25 Nov 2019 21:13

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Contributors

Author: M.A. Strickland
Author: M.R. Dressler
Author: M. Taylor

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