Regression models for the front grinding process on Grey Cast Iron block-engine

Abstract

This document describes the obtaining of different regression models for the surface roughness and wear
parameter in abrasive wheels Alumina (Al2O3) and silicon carbide (CSi) under the influence of cutting
parameters in the frontal grinding process. The methodology used in the present study is based on the use of
an experimental design (DOE) using two input variables (factors) feed rate and cut depth at three levels and
a categorical variable tool at two levels. The methods used to obtain models were linear regression, multiple
linear regression and logistic regression. The findings show that the type of tool and the speed of advance, have
greater correlation with surface quality and wear respectively. All the models establish a significant incidence
of these factors on the response variables with a confidence level of 95%. The results of the test show that with
the use of a carbide tool, a better surface quality can be obtained with the lowest wear parameter. Finally, an
SEM test showed the best surface topography obtained with the carbide tool compared to the alumina tool.