Statistical modelling of surface roughness of aluminium alloy and brass alloy

Abstract

This paper deals with the statistical modelling of surface roughness of face milled aluminium alloy and brass. By the application of central composite design and statistical analysis of experimental data, a regression model has been developed to calculate the surface roughness relating to three quantitative factors (depth of cut, feed rate and number of revolutions) and one qualitative factor (material type). A significant quadratic regression model with nonsignificant lack of fit has been derived. Also, for each level of qualitative factor (aluminium alloy and brass) regression models have been developed to predict surface roughness as a function of three numerical factors (depth of cut, feed rate and number of revolutions).

Use of Neural Networks in Prediction and Simulation of Steel Surface Roughness

“…The authors first conduct designed experiments. The Taguchi design of experiment is often used to reduce the time and cost of the experiments [12,13], but the central composite [14] and the full factorial design of experiments [1] are also used. The main purpose of designed experiments is to monitor the influence of controlled parameters on surface roughness [1,[15][16][17][18], but some authors also add other parameters like chip's characteristics [12], pre-tool wear vibrations [19], workpiece-tool vibration [20], lubrication-cooling condition [21] etc.…”