A geometrical model for surface roughness prediction when face milling Al 7075-T7351 with square insert tools. (July 2015)

• Record Type: Journal Article
• Authors: Muñoz-Escalona, Patricia; Maropoulos, Paul G.
• Is Part Of: Journal of manufacturing systems. Volume 36(2015)
• Page Start: 216
• Page End: 223
• Publication Date: 2015-07
• Subjects: Face milling -- Surface roughness -- Taguchi -- Tool run outs; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2014.06.011
• Languages: English
• ISSNs: 0278-6125

Abstract

Abstract: Surface quality is important in engineering and a vital aspect of it is surface roughness, since it plays an important role in wear resistance, ductility, tensile, and fatigue strength for machined parts. This paper reports on a research study on the development of a geometrical model for surface roughness prediction when face milling with square inserts. The model is based on a geometrical analysis of the recreation of the tool trail left on the machined surface. The model has been validated with experimental data obtained for high speed milling of aluminum alloy (Al 7075-T7351) when using a wide range of cutting speed, feed per tooth, axial depth of cut and different values of tool nose radius (0.8 mm and 2.5 mm), using the Taguchi method as the design of experiments. The experimental roughness was obtained by measuring the surface roughness of the milled surfaces with a non-contact profilometer. The developed model can be used for any combination of material workpiece and tool, when tool flank wear is not considered and is suitable for using any tool diameter with any number of teeth and tool nose radius. The results show that the developed model achieved an excellent performance with almost 98% accuracy in terms of predicting the surface roughness when compared to the experimental data.