Characterization of Signal Variations During the Self-Sharpening Process of Ceramic Diamond Grinding Wheels

This study investigates the real-time detection of self-sharpening signals in diamond ceramic grinding wheels used for SiC wafer planarization processing. Key grinding signals were captured and analyzed using a hydrophone (acoustic emission), accelerometers, and current transducers. RMS calculation and Welch's Fast Fourier Transform (Wfft) revealed that AE signals are the most reliable and sensitive indicators of self-sharpening, outperforming vibration and motor current signals. Significant power changes were observed in three AE frequency bands, offering insights into self-sharpening cycles. These findings provide a basis for integrating self-sharpening detection into intelligent grinding systems, enhancing efficiency and precision.