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Machine Learning Approach to the Prediction of Surface Roughness of Turned Glass/Basalt Epoxy Composites

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Title Machine Learning Approach to the Prediction of Surface Roughness of Turned Glass/Basalt Epoxy Composites
 
Creator Gadagi, Amith
Adake, Chandrashekar
 
Subject Composites
Production
Turning
Surface Roughness
Machine Learning
 
Description 805-815
In this work, the Machine Learning techniques namely Support Vector Regression, Random forest methodand Extreme
Gradient Boosting (XGBOOST) are utilized for the prediction of Surface Roughness in the turning process of Glass/Basalt
epoxy hybrid composites. The experiments were conducted in accordance with the Taguchi's L27 orthogonal array. The
experimental results indicates that, the surface roughness of the turned Glass/Basalt epoxy composites decreases with the
increase in Spindle speed, decrease in Feed rate and Depth of cut. It was also observed that feed rate has a greatest impact
and Depth of cut has a least effect over the surface roughness while the spindle speed moderately influenced the surface
roughness. From the results of Machine Learning models, it is evident that the Random forest model appears to be superior
with a Mean Absolute error and Maximum error of 4.96% and 7.73% respectively for testing data set.
 
Date 2024-02-21T06:17:01Z
2024-02-21T06:17:01Z
2024-02
 
Type Article
 
Identifier 0975-1017 (Online); 0971-4588 (Print)
http://nopr.niscpr.res.in/handle/123456789/63355
https://doi.org/10.56042/ijems.v30i6.2182
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source IJEMS Vol.30(6) December