Optimization-assisted VMD frameworks for chatter detection using multiscale entropy features

Abstract

This study presents an analytical comparison of three meta-heuristic optimization-based signal processing frameworks based on variational mode decomposition (VMD): particle swarm optimization-VMD (PSO-VMD), grey wolf optimizer-VMD (GWO-VMD), and whale optimization algorithm-VMD (WOA-VMD). The authors performed 44 milling experiments under variable machining parameters (spindle speed 500–3000 r/min; feed rate 20–120 mm/min; depth of cut 0.3–1.8 mm). The acoustic signals generated by each experiment were captured with a high-sensitivity microphone. The authors applied the three meta-heuristics to optimize the VMD parameters. The hybrid optimization-VMD methods were employed to adaptively adjust the parameters to efficiently decompose complex machining signals. Among the three methods, PSO-VMD produced the best results due to optimal convergence when selecting a mode number (K = 8) and alpha values (range of 150–4300). The extracted intrinsic mode functions (IMFs) produced the tooth passing frequency (TPF) and its harmonics and the dominant chatter frequency (923 Hz) as well as two sidebands (1423 Hz & 2248 Hz). Using multiscale permutation entropy (MPE) to analyze the time series data of the IMF components enabled the identification of stable versus unstable cutting regimes as well as the identification of significant changes in entropy as machining conditions changed. The authors report that PSO-VMD was superior to both GWO-VMD and WOA-VMD in terms of modal separation, spectral clarity and robustness to noise with a fitness score of 0.0657 being reported for PSO-VMD vs. 0.0703 for GWO-VMD and 0.0841 for WOA-VMD. The application of the PSO-VMD-MPE framework provides a viable, interpretable and computationally efficient methodology to monitor chatter in real-time within IoT-enabled machining environments.