Artificial neural network based prediction on tribological properties of polycarbonate composites reinforced with graphene and boron carbide particle

Polycarbonate (PC) composites filled with Graphene (up to 10 vol%) and Boron carbide particles (up to 5 vol%) are fabricated by Injection molding and subsequently compressed and quenched. An interactive outcome of incorporated Graphene (C) and Boron carbide particles (B4C) are reported. The lowest w...

Full description

Saved in:
Bibliographic Details
Main Authors: Zakaulla, Mohamed, Parveen, Fathima, Amreen, Harish, Ahmad, Nehal
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural network
Frictional coefficient
Graphene
Parameter prediction
Polycarbonate
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Polycarbonate (PC) composites filled with Graphene (up to 10 vol%) and Boron carbide particles (up to 5 vol%) are fabricated by Injection molding and subsequently compressed and quenched. An interactive outcome of incorporated Graphene (C) and Boron carbide particles (B4C) are reported. The lowest wear was prevailed for the composition of PC with 10 vol% C and 5 vol% of B4C. Artificial neural network (ANN) proficiency is employed to predict wear properties of polycarbonate based composites. Frictional coefficient and wear are successfully computed by well trained ANN from the experimental database of Graphene and Boron carbide particles reinforced polycarbonate composite. 3-D plots for the predicted Frictional coefficient and wear as a function of testing conditions and material compositions were constituted. The final results are very much in accord with the measured data. A well trained ANN is anticipated to aid for optimum design of composite materials for consistent parameter studies.
ISSN:2214-7853
2214-7853
DOI:10.1016/j.matpr.2019.11.276