TY - JOUR
T1 - Effect of B4C/Gr on Hardness and Wear Behavior of Al2618 Based Hybrid Composites through Taguchi and Artificial Neural Network Analysis
AU - Ballupete Nagaraju, Sharath
AU - Kodigarahalli Somashekara, Madhu
AU - Puttegowda, Madhu
AU - Manjulaiah, Hareesha
AU - Kini, Chandrakant R.
AU - Channarayapattana Venkataramaiah, Venkatesh
N1 - Funding Information:
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Publisher Copyright:
© 2022 by the authors.
PY - 2022/12
Y1 - 2022/12
N2 - Artificial neural networks (ANNs) have recently gained popularity as useful models for grouping, clustering, and analysis in a wide range of fields. An ANN is a kind of machine learning (ML) model that has become competitive with traditional regression and statistical models in terms of useability. Lightweight composite materials have been acknowledged to be the suitable materials, and they have been widely implemented in various industrial settings due to their adaptability. In this research exploration, hybrid composite materials using Al2618 reinforced with B4C and Gr were prepared and then evaluated for hardness and wear behavior. Reinforced alloys have a higher (approximately 36%) amount of ceramic phases than unreinforced metals. With each B4C and Gr increase, the wear resistance continued to improve. It was found that microscopic structures and an appearance of homogenous particle distribution were observed with an electron microscope, and they revealed a B4C and Gr mixed insulation surface formed as a mechanically mixed layer, and this served as an effective insulation surface that protected the test sample surface from the steel disc. The ANN and Taguchi results confirm that load contributed more to the wear rate of the composites.
AB - Artificial neural networks (ANNs) have recently gained popularity as useful models for grouping, clustering, and analysis in a wide range of fields. An ANN is a kind of machine learning (ML) model that has become competitive with traditional regression and statistical models in terms of useability. Lightweight composite materials have been acknowledged to be the suitable materials, and they have been widely implemented in various industrial settings due to their adaptability. In this research exploration, hybrid composite materials using Al2618 reinforced with B4C and Gr were prepared and then evaluated for hardness and wear behavior. Reinforced alloys have a higher (approximately 36%) amount of ceramic phases than unreinforced metals. With each B4C and Gr increase, the wear resistance continued to improve. It was found that microscopic structures and an appearance of homogenous particle distribution were observed with an electron microscope, and they revealed a B4C and Gr mixed insulation surface formed as a mechanically mixed layer, and this served as an effective insulation surface that protected the test sample surface from the steel disc. The ANN and Taguchi results confirm that load contributed more to the wear rate of the composites.
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U2 - 10.3390/catal12121654
DO - 10.3390/catal12121654
M3 - Article
AN - SCOPUS:85144905278
SN - 2073-4344
VL - 12
JO - Catalysts
JF - Catalysts
IS - 12
M1 - 1654
ER -