Prediction and optimization on tribological behaviour of kenaf/carbon fiber reinforced epoxy matrix hybrid composites
The awareness on sustainability of the environment among the researchers leads to the exploration of natural fiber composite materials. Hybridization of synthetic fiber and natural fiber is one of the potential strategies to enhance the mechanical properties as well as the degradability of suc...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Trans Tech Publications Ltd
2022
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/98042/1/98042_Prediction%20and%20optimization%20on%20tribological.pdf http://irep.iium.edu.my/98042/2/98042_Prediction%20and%20optimization%20on%20tribological_SCOPUS.pdf http://irep.iium.edu.my/98042/ https://www.scientific.net/KEM.908.3 |
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| Summary: | The awareness on sustainability of the environment among the researchers leads to the
exploration of natural fiber composite materials. Hybridization of synthetic fiber and natural fiber is
one of the potential strategies to enhance the mechanical properties as well as the degradability of
such composite materials. However, less information concerning the optimization of tribological
properties of this hybrid composite is available in literature. The aim of this study is to propose a
statistical model to predict and optimize wear and coefficient of friction of kenaf/carbon reinforced
epoxy composite. The value of parameters; load and sliding velocity ranges from 10 to 30 N and 20.9
to 52.3 m/s, respectively, are used to assess wear and coefficient of friction (COF) of different
stacking sequences using the Analysis of Variance (ANOVA). The tribological test was conducted
using a pin-on-disc tribometer. Multifactorial design analysis was employed to optimize the test control variables. It was found that, the optimized factors that affects the coefficient of friction and wear is at load 30 N and sliding velocity of 52.36 m/s. The proposed statistical models for wear and
COF have 99.5% and 97.6% reliability, respectively. The generated equation models are bounded within the wear test control factors and ranges. The outcome from this study will be very useful for main parameter prediction for an optimized wear and COF |
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