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...

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Autores principales:	Pérez-Salinas,Cristian, Valencia Nuñez,Roberto, Analuiza Maiza,Oscar, Fiallos Zamora,Luis, Paredes Zumbana,Jorge
Lenguaje:	English
Publicado:	Universidad de Tarapacá. 2019
Materias:	Regression models grinding wear parameter surface roughness cutting parameter
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052019000300510
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spelling	oai:scielo:S0718-330520190003005102019-11-06Regression models for the front grinding process on Grey Cast Iron block-enginePérez-Salinas,CristianValencia Nuñez,RobertoAnaluiza Maiza,OscarFiallos Zamora,LuisParedes Zumbana,Jorge Regression models grinding wear parameter surface roughness cutting parameter 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.info:eu-repo/semantics/openAccessUniversidad de Tarapacá.Ingeniare. Revista chilena de ingeniería v.27 n.3 20192019-09-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052019000300510en10.4067/S0718-33052019000300510
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Regression models grinding wear parameter surface roughness cutting parameter
spellingShingle	Regression models grinding wear parameter surface roughness cutting parameter Pérez-Salinas,Cristian Valencia Nuñez,Roberto Analuiza Maiza,Oscar Fiallos Zamora,Luis Paredes Zumbana,Jorge Regression models for the front grinding process on Grey Cast Iron block-engine
description	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.
author	Pérez-Salinas,Cristian Valencia Nuñez,Roberto Analuiza Maiza,Oscar Fiallos Zamora,Luis Paredes Zumbana,Jorge
author_facet	Pérez-Salinas,Cristian Valencia Nuñez,Roberto Analuiza Maiza,Oscar Fiallos Zamora,Luis Paredes Zumbana,Jorge
author_sort	Pérez-Salinas,Cristian
title	Regression models for the front grinding process on Grey Cast Iron block-engine
title_short	Regression models for the front grinding process on Grey Cast Iron block-engine
title_full	Regression models for the front grinding process on Grey Cast Iron block-engine
title_fullStr	Regression models for the front grinding process on Grey Cast Iron block-engine
title_full_unstemmed	Regression models for the front grinding process on Grey Cast Iron block-engine
title_sort	regression models for the front grinding process on grey cast iron block-engine
publisher	Universidad de Tarapacá.
publishDate	2019
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052019000300510
work_keys_str_mv	AT perezsalinascristian regressionmodelsforthefrontgrindingprocessongreycastironblockengine AT valencianunezroberto regressionmodelsforthefrontgrindingprocessongreycastironblockengine AT analuizamaizaoscar regressionmodelsforthefrontgrindingprocessongreycastironblockengine AT fialloszamoraluis regressionmodelsforthefrontgrindingprocessongreycastironblockengine AT paredeszumbanajorge regressionmodelsforthefrontgrindingprocessongreycastironblockengine
_version_	1714203473111678976

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

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