<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The surface roughness is a very significant indicator of surface quality. It represents an essential exploitation requirement and influences technological time and costs, i.e. productivity. For that reason, the main objective of this paper is to analyse the influence of face milling cutting parameters (number of revolution, feed rate and depth of cut) on the surface roughness of aluminium alloy. Hence, a statistical (regression) model has been developed to predict the surface roughness by using the methodology of experimental design. Central composite design is chosen for fitting response surface. Also, numerical optimization considering two goals simultaneously (minimum propagation of error and minimum roughness) was performed throughout the experimental region. In this way, the settings of cutting parameters causing the minimum variability in response were determined for the estimated variations of the significant regression factors.</p></abstract>

The surface roughness is a very significant indicator of surface quality. It represents an essential exploitation requirement and influences technological time and costs, i.e. productivity. For that reason, the main objective of this paper is to analyse the influence of face milling cutting parameters (number of revolution, feed rate and depth of cut) on the surface roughness of aluminium alloy. Hence, a statistical (regression) model has been developed to predict the surface roughness by using the methodology of experimental design. Central composite design is chosen for fitting response surface. Also, numerical optimization considering two goals simultaneously (minimum propagation of error and minimum roughness) was performed throughout the experimental region. In this way, the settings of cutting parameters causing the minimum variability in response were determined for the estimated variations of the significant regression factors.

Predicting the Surface Quality of Face Milled Aluminium Alloy Using a Multiple Regression Model and Numerical Optimization

Mechanical Engineering Faculty in Slavonski Brod, University of Osijek, Trg I. Brlic-Mazuranic 2, HR 35000 Slavonski Brod, Croatia

Measurement Science Review

The surface roughness is a very significant indicator of surface quality. It represents an essential exploitation requirement and influences technological time...

Predicting the Surface Quality of Face Milled Aluminium Alloy Using a Multiple Regression Model and Numerical Optimization

Publié en ligne: 02 nov. 2013

Pages: 265 - 272

DOI: https://doi.org/10.2478/msr-2013-0039

Mots clés
Surface roughness, face milling, aluminium alloy, experimental design, regression model

This content is open access.

Predicting the Surface Quality of Face Milled Aluminium Alloy Using a Multiple Regression Model and Numerical Optimization

Publié en ligne: 02 nov. 2013

Pages: 265 - 272

DOI: https://doi.org/10.2478/msr-2013-0039

Mots clésSurface roughness, face milling, aluminium alloy, experimental design, regression model

This content is open access.

Mots clés
Surface roughness, face milling, aluminium alloy, experimental design, regression model