Methodological Foundations for the Development of a Measurement System for Recording Acoustic Emission Signals during Machining

The study is aimed at investigating the correlation between AE signals and machining parameters—such as cutting speed, depth, feed rate, and tool condition—in order to optimize the process and predict tool wear. Reliable, real-time monitoring criteria were established to enhance production efficiency and ensure product quality. To achieve these objectives, an experimental setup was designed and implemented, comprising a piezoelectric sensor (with a central frequency of 200–300 kHz and a passband of 100–700 kHz), an amplifier with a gain of 40–60 dB, and a high-speed analog-to-digital converter operating at a minimum sampling rate of 2 MHz. A computer-based data acquisition system was integrated into the measurement complex to perform both spectral and amplitude analyses of AE signals. Experimental tests were performed on cylindrical workpieces machined on a lathe under varying cutting conditions, with continuous AE signal registration and subsequent Fourier transform analysis. It was demonstrated that an increase in cutting speed shifts dominant frequency peaks to higher ranges and raises the overall amplitude of the AE signals. The experimental setup was also shown to be capable of detecting critical process conditions such as tool overload and early-stage tool wear, thereby laying the groundwork for automated quality control systems in metal machining. In conclusion, a robust methodological framework for the use of AE signals in machining process monitoring has been established. The developed measurement complex proved effective in capturing and analyzing AE data in real-time, enabling the detection of anomalies and tool degradation. These findings underscore the potential of AE-based monitoring systems to enhance machining accuracy and prolong tool life. Future research is recommended to focus on the integration of machine learning techniques for improved signal classification and further automation of the monitoring process.