Performance Analysis of Nano Reinforced Polyether Ether Ketone (PEEK) Gear

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Date

2024-04

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Addis Ababa University

Abstract

Current research trend is shifted in finding better materials and enhanced material for gear application. Polymer nanocomposites materials are being used as alternative gear materials for the application of power transmission due to their numerous advantages. Polymer nanocomposites are lighter than conventional materials. They have high degree of stiffness and strength compared with high-density materials. In this study, best performing material is chosen scientifically comparing different materials that are used for power transmission that can sustain high cyclic load & wearing from recently developed literatures. Then the material property is predicted applying machine learning method. Chemical composition consisting of Polyether ether ketone (PEEK) polymer matrix composite material and the 4% nano filler multi-walled carbon nano tube (MWCNT), 3% Zicronia (ZrO2) and 5% Silica (SiO2) by weight is selected and the material mechanical properties is obtained from the trained machine learning model. Using the data obtained analysis of the gear with different loading condition is simulated with ANSYS software and the results are compared with recently developed gear materials for power transmission. The result shows that the addition of nano particles in PEEK polymer can improve the property of the material. The material gear life cycle is found to be one million cycles with loading condition of 1,450 rpm and 21 Nm torque and the maximum bending stress at this load is recorded for at the transient structural FEM analysis which is 75.22 MPa while in static structural FEM analysis is obtained 64.48 MPa. Whereas for contact and frictional stress close results are obtained. The polymer nanocomposite gear material is appeared to be potential gear material with enhanced material property, good strength and durability for the application of power transmission.

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Keywords

PEEK Nanocomposite, ANN, Material Property Prediction, FEM, Gear

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