Mathematical modelling to predict the roughness average in micro milling process

4th ModTech International Conference - Modern Technologies in Industrial Engineering

Burlacu, C.^1 ; Iordan, O.^1

Gheorghe Asachi Technical University of Iasi-Romania, Department of Machine Manufacturing Technology, Blvd. Mangeron, No. 59A, Iasi

700050, Romania^1

关键词: Chemical characteristic;    Chemical compositions;    Independent parameters;    Independent variables;    Multiple regression methods;    Statistical software;    Surface roughness parameters;    Surface roughness profiles;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/145/7/072004/pdf
DOI  :  10.1088/1757-899X/145/7/072004

来源: IOP

【 摘 要 】

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 (Ra). This paper presents the experimental results obtained at micro milling of the C45W steel and the ways to determine the Raparameter 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 Ra parameter and the process variables. The mathematical model were developed, using multiple regression method with four independent variables D, v, ap, fz; 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.