Experimental investigation of tool vibration and surface roughness in the precision end-milling process using the singular spectrum analysis

The vibrations on the cutting tool have a momentous influence for the surface quality of workpiece with respect to surface profile and roughness during the precision end-milling process. Singular spectrum analysis (SSA) is a new non-parametric technique of time series analysis and forecasting. The significant features of the cutting tool vibration signals from the sensors are extracted and transformed from the SSA-processed vibration signals. In the present study, SSA is applied to extract and transform the raw signals of the vibrations on the cutting tool for investigating the relationship between tool vibration and surface roughness in the precision end-milling process of hardened steel SCM440. In this experimental investigation, the spindle speed, feed rate, and cutting depth were chosen as the numerical factor; the cutting feed direction and holder type were regarded as the categorical factor. An experimental plan consisting of five-factor (three numerical plus two categorical) d-optimal design based on the response surface methodology was employed to carry out the experimental study. A micro-cutting test was conducted to visualize the effect of vibration of tooltip on the performance of surface roughness. With the experimental values up to 95% confidence interval, it is fairly well for the experimental results to present the mathematical models of the tool vibration and surface roughness. Results show that the effects of feed rate and cutting depth provide the reinforcement on the overall vibration to cause the unstable cutting process and exhibit the result of the worst machined surface. The amplitude of vibration signals along the cutting feed direction is generally larger than that along other direction. The spindle speed and tool holder type affect the stability of cutting tooltip during the cutting process.