OBRABOTKAMETALLOV Vol. 26 No. 3 2024 technology Fig. 7. The results of VA diagnostics: a – frequency response with wear less than 4 μm; б – frequency response with wear more than 4 μm; в – vibration acceleration а b c of view of computational resources and accuracy, as a result, the Hamming window function is determined as the most suitable for analyzing the machining process. The dependences of the frequency response of the acoustic signal and surface roughness on the degree of tool wear are established, on the basis of which conclusions can be drawn about the condition of the cutting tool at stages close to critical in terms of the quality of the resulting surfaces. Although the study was aimed at determining the relationships between tool wear level, tool vibroacoustic signal and surface finish quality, the results of this study can be used to develop new techniques for monitoring tool wear, increasing the efficiency of the material processing process and improving the quality of finished products. Also, the study made it possible to formulate a sequence of actions for VA analysis, which contributes to a more accurate identification and analysis of acoustic characteristics associated with the cutting process: 1) install sensors (accelerometers, microphones) near the working area of technological equipment to record VA signals; 2) collect time data of VA signals; 3) convert an analog signal to a digital one using an analog-to-digital converter; 4) apply a window function to the collected data to minimize spectral leakage before performing FFT; 5) perform FFT to convert time signal to frequency domain; 6) analyze the spectrum to identify dominant frequency components associated with instrument condition; 7) apply filters to isolate frequency components of interest (apply a low-pass filter to remove highfrequency noise; apply a band-pass filter to highlight frequencies associated with normal tool operation and anomalies; apply a high-pass filter to remove low-frequency noise and vibration.)
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