Stability analysis in milling process using spline based local mean decomposition (SBLMD) technique and statistical indicators

In the present work, a novel online monitoring system has been developed using a new modified spline based local mean decomposition technique for the identification of tool chatter onset pertaining to the three input milling parameters viz. feed rate, axial depth of cut and spindle speed. Acquired sound signals of machining at various cutting parameters are processed using a Spline Based Local Mean Decomposition (SBLMD) technique. A series of product functions are extracted from the processed signal. Prominent PF's are combined to reconstruct the signal representing chatter. Reconstructed chatter signal has been analysed to extract tool chatter features. Finally, online monitoring of tool chatter is executed considering the ascertained chatter severity threshold. Viability of the proposed methodology is validated by performing more experiments and the results indicated that it can be well adopted for the online monitoring of tool chatter.

Automated summary

A novel online monitoring system has been developed using a new modified spline-based local mean decomposition technique for the identification of tool chatter onset with consideration of three input milling parameters. Experimental results indicate that the proposed methodology can be effectively adopted for online monitoring of tool chatter.