Mathematical modelling to predict the roughness average in micro milling process

Abstract

Abstract. Surface roughness plays a very important role in micro milling process and in any machining process, because indicates the state of the machined surface. Many surface roughness parameters that can be used to analyse a surface, but the most common surface roughness parameter used is the average roughness (R a ). This paper presents the experimental results obtained at micro milling of the C45W steel and the ways to determine the R a parameter with respect to the working conditions. The chemical characteristics of the material were determined from a spectral analysis, chemical composition was measured at one point and two points, graphical and tabular. A profilometer Surtronic 3+ was used to examine the surface roughness profiles; the effect of independent parameters can be investigated and can get a proper relationship between the R a parameter and the process variables. The mathematical model were developed, using multiple regression method with four independent variables D, v, a p , f z ; the analysis was done using statistical software SPSS. The ANOVA analysis of variance and the F-test was used to justify the accuracy of the mathematical model. The multiple regression method was used to determine the correlation between a criterion variable and the predictor variables. The prediction model can be used for micro milling process optimization.

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