Surface quality evaluation in meso-scale end-milling operation based on fractal theory and the Taguchi method

Abstract

This paper presents a study of the Taguchi application to optimize surface quality based on fractal theory in meso-scale end-milling operation. Usually, the surface quality is characterized by roughness, but in the meso-scale area, the size of the workpiece will be difficult to meet the requirements of the sampling length, which will have a huge influence on the characterization results. Although there are many algorithms for adjusting, the operation process is very cumbersome; for this reason, characterization method based on fractal theory is introduced and applied to evaluate the machined surface in the research. Also, the Taguchi method is applied to search the optimal parameters combination, during which spindle speed, depth of cut, and feed rate are considered as the control factors. Specifically, firstly, the machining surface characterization method based on fractal theory is introduced in details; then, an orthogonal array of L25(53) is used for the experimental study; through ANOVA analyses, the significant factors which affect surface quality are established, and the optimal cutting parameter combination is determined; later, confirmation tests are carried out to verify the applicability of the characterization methods for the meso-scale cutting surface. Research shows that fractal characterization method is better to evaluate the surface quality in the meso-scale.