Artificial Intelligent Systems and Machine Learning

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


Affiliations
1 Department of Mechanical Engineering, Manoharbhai Patel Institute of Engineering and Technology, (MS), India
2 VM Institute of Engineering and Technology, Nagpur (MS), India
3 Maharashtra Academy of Engineering, Alandi, Pune (MS), India
     

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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.

Keywords

Artificial Neural Network (ANN), Alumina (Al2O3), Design of Experiments (DoE) – Taguchi Method, Hexagonal Boron Nitride (HBN), Silicon Nitride (Si3N4).
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  • Modeling of Wear Performance of Si3N4-hBN Composite Using Artificial Neural Network (ANN)

Abstract Views: 176  |  PDF Views: 3

Authors

Sachin Ghalme
Department of Mechanical Engineering, Manoharbhai Patel Institute of Engineering and Technology, (MS), India
Ankush Mankar
VM Institute of Engineering and Technology, Nagpur (MS), India
Y .J. Bhalerao
Maharashtra Academy of Engineering, Alandi, Pune (MS), India

Abstract


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.

Keywords


Artificial Neural Network (ANN), Alumina (Al2O3), Design of Experiments (DoE) – Taguchi Method, Hexagonal Boron Nitride (HBN), Silicon Nitride (Si3N4).