Improved STFT analysis using time-frequency masking for chatter detection in the milling process

In the field of machining, the purposeful increase in cutting depth and rotation speed can potentially cause chatter vibration. Chatter lowers surface quality, tool and spindle life, and produces excessive noise, which limits the productivity of the machining process. In this paper, an improved Short-Time Fourier Transform (STFT) involving time-frequency masking and adaptive thresholding is proposed. Initially, hammering tests were undertaken to ascertain the inherent natural frequency of the cutting system. Subsequently, milling tests were conducted across varied conditions utilizing an accelerometer sensor and a microphone (capturing sound signals) to acquire the requisite experimental data. A 3-level Variational Mode Decomposition (VMD) was used for pre-processing due to its inherent capabilities for noise reduction. The chatter threshold is established by averaging the total intrinsic mode function (IMF) energy levels that have been selected. To demonstrate the efficacy of the proposed method, root mean square (RMS) and skewness were calculated as chatter indicators. The improved STFT time-frequency representation (TFR) was compared to the conventional TFR. Notably, both indicators demonstrate effectiveness and chatter sensitivity, while the improved STFT offers greater time-frequency resolution than its conventional counterpart and can be successfully used for monitoring the milling process.