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Ghalme, S., Mankar, A., Bhalerao, Y. 
ORCID: https://orcid.org/0000-0002-0743-8633 and Patil, M.
(2018)
Integrated Taguchi-artificial neural network approach for modeling and optimization of wear performance of Si 3 N 4 -hBN composite.
In:
AIP Conference Proceedings.
UNSPECIFIED.
Abstract
During action or movement in the artificial joint there is generation of wear particles creating a serious issue like aseptic loosening of joint. Silicon nitride (Si3N4) is proposed as an alternative material for knee/hip joint replacement. Si3N4 against steel (ASTM 316L) is a material combination in the category of Ceramic on Metal (CoM) for artificial joint. The work covered in this paper, try to obtain the optimum value of % hexagonal boron nitride (hBN) by volume to be mixed in Si3N4 to reduce wear against steel. The experiments were planned as per Design of Experiments (DoE) – Taguchi method to obtain the optimum combination of % volume of hBN and load. Taguchi analysis presents 12% volume of hBN and 15N load is optimum to minimize wear loss. Using experimental results, artificial neural network (ANN) model trained, tested and implemented to predict results of volumetric wear loss (VWL) at different loading condition.
| Item Type: | Book Section |
|---|---|
| Faculty \ School: | Faculty of Science > School of Engineering (former - to 2024) |
| UEA Research Groups: | Faculty of Science > Research Groups > Materials, Manufacturing & Process Modelling |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 14 Jan 2020 06:19 |
| Last Modified: | 07 Nov 2024 12:49 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/73626 |
| DOI: | 10.1063/1.5058240 |
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