Abstract:
CFRPs, or carbon fiber reinforced polymers, are renowned for having superior
wear characteristics. In this study, we compared the wear properties of polycar bonate (PC)/acrylonitrile butadiene styrene (ABS) modified di-glycidyl ether of
bisphenol A (DGEBA) based CFRPs with electrophoretically deposited (EPD) car bon fibers. We studied the wear rate (WR) and coefficient of friction (COF) of
EPD-modified CF with flexible silanized graphene oxide (SGO) bonds using pin on-disk experiments. The findings showed significant decreases in COF and WR of
25% and 34%, respectively.
Additionally, the behaviour of the CFRP system with PC/ABS varied depending
on the blend ratios. The COF and WR of blends such 70/30, 50/50, and 30/70
showed varied tendencies, however, 10/90 and 90/10 showed consistent decremental
patterns, with 90/10 achieving maximum reductions of 19% and 37.9%, respectively.
We also looked at how applied load and sliding speed affected wear severity, which
changed the wear mechanism from abrasion to delamination. Two testing scenarios
were used in the study: room temperature (RT) and cryo-treated (CT).
The sample composition was found to be the most important parameter in terms of
volume loss using analysis of variance (ANOVA). A Taguchi analysis was also car ried out to optimize the parameters and provide the desired result. We used three
predictive models to forecast wear behaviour: the Levenberg-Marquardt algorithm,
Artificial Neural Network, and linear regression. The Levenberg-Marquardt algo rithm showed the best performance based on mean squared error (MSE) loss, with
2.7% MSE for RT and 2.15% MSE for CT, demonstrating its potential for precise
wear behaviour prediction in CFRP composites.
The Levenberg-Marquardt algorithm has a lot of potential for forecasting how CFRP
composites will wear. This discovery aids in the comprehension and improvement of
CFRP materials for a range of applications needing dependable wear performance.
