Life Enhancement of Partial Removable Denture made by Biomaterials Reinforced by Graphene Nanoplates and Hydroxyapatite with the Aid of Artificial Neural Network

Muhannad Al-Waily†*, Iman Q. Al Saffar‡, Suhair G. Hussein‡, Mohsin Abdullah Al-Shammari‡


†Department of Mechanical Engineering, Faculty of Engineering, University of Kufa, Iraq

‡University of Baghdad, College of Engineering, Department of Mechanical Engineering, Iraq

Corresponding Author Email: muhanedl.alwaeli@uokufa.edu.iq

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The fatigue behavior of removable partial denture is very important parameter so that it must be invested, since, it is part of human body exposed to a variable load with time, then, lead to a failure due to dynamic load. So, it was necessary to modify the dynamic characterization for biomaterials used for manufacturing this part. The aim of this work is modifying the fatigue strength and life with high values by low volume fraction reinforcement by Nanomaterials. Thus, the two types for Nano materials, GNP (Graphene Nanoplates) and HAP (Hydroxyapatite), used to modifying the dynamic properties for biomaterials used, with weight fraction of (0.25 ,0.5, 0.75, 1 and 1.25%). Two techniques (experimental work and artificial neural network) were used to estimate the fatigue behavior for the specimens with the effect of variation of the reinforcement Nanomaterials types and the volume fraction of them. The experimental work is used to manufacture fatigue samples, and then, using fatigue test to estimate the fatigue strength with various Nanomaterials parameters effect. Then, artificial neural networks (ANN) technique is also used to calculate the fatigue life of the specimens, and then, a comparison is done between the two techniques. Very little discrepancy between experimental and ANN results is got from the results, with a value did not exceed about (0.64%). Finally, the modifying process of the fatigue behavior, life and strength, lead to about (28%) with reinforcement by Nano materials, also it can be seen from the obtained results that the modifying of fatigue characterization with reinforcement by (GNP) is better than the modifying by reinforcement by (HAP), with an increment of about 6%.