Modeling of Wear Performance of Si3N4-hBN Composite Using Artificial Neural Network (ANN)

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Wear particles generated due to rolling/sliding motion between artificial joint leads to joint failure, which need to be minimised to extend the joint life. Silicon nitride (Si3N4) is non-oxide ceramic suggested as a new alternative for hip/knee joint replacement. Hexagonal Boron Nitride (hBN) is suggested as a solid additive lubricant to improve the wear performance of Si3N4. In this paper attempt has been made to evaluate the optimum proportion of % hBN in Si3N4 to minimise wear volume loss (WVL) against alumina (Al2O3) counterface. The experiments were conducted according to Design of Experiments (DoE) – Taguchi method and using the experimental results artificial neural network (ANN) trained and simulated for the different condition to predict wear volume loss in the Si3N4-hBN composite. Taguchi method presents 15N load and 8% hBN to minimise WVL of Si3N4. To confirm these levels, trained ANN simulated to validate the control parameters suggested by Taguchi method.
Original languageEnglish
Pages (from-to)57-61
Number of pages5
JournalArtificial Intelligent Systems and Machine Learning
Issue number2
Publication statusPublished - 2016

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