Product life cycle: machining processes monitoring and vibroacoustic signals filterings

OBRABOTKAMETALLOV technology Vol. 26 No. 3 2024 The tool material used is CVD coated carbide BC20HT, application range according ISO is K10–K20. The tool overhang ratio was assumed to be l/D = 4. The feed per tooth was fz = 0.2 mm/tooth, ap = 10 mm, t = 0.4 mm. The spindle speed (n) was 1,500 min–1. During milling cutting tools were measured along its length and radius using a Heidenhain TT140 contact sensor to monitor the degree of tool wear. Vibroacoustic diagnostics were carried out using the Spectrum Analyzer ZetLab 017–U2 device based on a DMG DMU (Germany) 50 Ecoline machining center. The amplitude of the acoustic signal A (dB), changing over time t (s), vibration acceleration a (m/s2), signal frequency ω (Hz) and the microrelief parameter – roughness – Rz (μm) were used as output indicators of the efficiency of machining. The range of perceived frequencies of the microphone was 20 Hz÷20 kHz, resolution was 16 bits and sampling frequency was 44.1/48 kHz. For the optimal balance between time and frequency resolution, the FFT size was chosen to be 16,384, which ensures sufficient detail and accuracy of the analysis. Roughness measurements after surface milling were carried out using a TIME TR 200 profilometer; for this device, the error according to the standard is 3 %. The primary profile filtering process was performed using a 50 % Gaussian filter. Results and discussion When machining by cutting, vibrations and noises that occur during operation play an important role. Digital signal processing techniques including FFT [77], window functions and filtering were used to analyze these signals. Fig. 4 clearly shows that the microphone records several sound sources, including sounds around the technological equipment, the drive system of the technological equipment, the spindle, the tool, and the cutting process. Therefore, for a more accurate analysis, an acoustic signal within the frequency range characteristic of the cutting process was considered. This allows to better identify specific characteristics and features that may not be obvious in the wider frequency spectrum. From Fig. 4 it can be seen that background noise can be identified in the range of 0÷2 kHz, this specified part of the signal can be easily filtered without losing the main signal. Spectrum analysis reveals dominant frequencies and amplitudes that may indicate resonances, defects and tool wear. After spectral subtraction of noise and filtering of the acoustic signal, the frequency response of oscillations is characterized by three main Fig. 3. The process of software processing of an acoustic signal Fig. 4. Frequency response of the acoustic signal: 1 – Initial; 2 – After noise removal; 3 – Filtered; 4 – Filtered and normalized

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