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Composites: Mechanics, Computations, Applications: An International Journal
ESCI SJR: 0.193 SNIP: 0.497 CiteScore™: 0.39

ISSN Print: 2152-2057
ISSN Online: 2152-2073

Composites: Mechanics, Computations, Applications: An International Journal

DOI: 10.1615/CompMechComputApplIntJ.v6.i3.20
pages 193-205

PREDICTION OF EROSION WEAR OF GRANITE-FILLED JUTE−EPOXY COMPOSITES USING AN ARTIFICIAL NEURAL NETWORK

Sucharita Sanghamitra Sahoo
School of Mechanical Engineering, KIIT University, Bhubaneswar, 751024, India
Arun Kumar Rout
Department of Production Engineering, Veer Surendra Sai University of Technology, Burla-768018, India

ABSTRACT

The erosion wear process is considered as a complex nonlinear problem due to its operating variables. The wear process depends on various parameters such as impact velocity, impingement angle, material, erodent size, etc. In order to obtain a minimum erosion rate, experiments have to be conducted on a material with a combination of these parameters. Therefore, it becomes expensive and time consuming for finding out the minimum erosion rate of a material. In this regard, an artificial neural network (ANN) is a robust tool for predicting the erosion rate of a material. ANN is capable of representing nonlinear systems and can be applied to a wide variety of fields. In the present work, waste granite powder is used as a filler in a jute fiber-reinforced−epoxy composite. Four different weight proportions of granite (0, 5, 10, and 15 wt.%) are used to fabricate four different composites. It is observed that the predicted values of erosion rate have shown good agreement with experimental values.


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