An ensemble approach to maximize metal removal rate for chatter free milling

Abstract

In the present work, a new ensemble approach is adopted to obtain an optimal set of machining parameters resulting in chatter free milling at higher Metal Removal Rate (MRR). In order to achieve this, a modified LMD based on cubic spline interpolation is used to analyze the recorded audio signals during machining. Further, these decomposed chatter signals are explored to ascertain chatter severity in terms of a new dimensionless chatter indicator (CI). Total 27 experiment based on full factorial design are performed considering 3 levels of each machining parameters viz. axial depth of cut (D), feed rate (F) and spindle speed (N). CI and MRR are calculated for these 27 tests. Further, regression models of CI and MRR are developed using RSM (Response Surface Methodology) approach. Finally, multi-objective particle swarm optimization (MOPSO) is invoked to optimize these regression models for obtaining best possible range of input parameters for minimal chatter and maximum MRR. Moreover, to validate the presented methodology, more tests are performed considering the ascertained optimal set of parameters. Verification results proved that the developed optimal solution is feasible and can be used for higher productivity with chatter free milling.