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.

acoustic emission, metal machining, piezoelectric sensor, spectral analysis, amplitude analysis, tool wear monitoring

Full Text:

PDF

1. Makovetskyi, O. S. (2015). Mechanics of Metal Cutting. Kyiv: Naukova Dumka. 320 [in Ukrainian].

2. Kalpakjian, S., & Schmid, S. R. (2016). Manufacturing Processes for Engineering Materials. (6th ed.). Pearson.

3. Trent, E. M., & Wright, P. K. (2000). Metal Cutting. (4th ed.). Butterworth-Heinemann [in English].

4. Hase, A., Kitajima, T., & Ogawa, T. (2014). Analysis of acoustic emission during orthogonal cutting of mild steel . Journal of Materials Processing Technology, 214, 1758–1766.

5. Li, X. (2002). A brief review: acoustic emission method for tool wear monitoring during turning . International Journal of Machine Tools and Manufacture, 42, 157–165.

6. Shipunov, G. D., Tomashevskyi, V. M., & Kozhevnykov, V. A. (2018). Control and Diagnosis of the Cutting Process by the Acoustic Emission Method . Bulletin of NTU “KhPI”, 4, 67–72 [in Ukrainian].

7. Pawar, P. J., & Joshi, S. S. (2010). Analysis of acoustic emission signals and surface texture generated in turning process of metal matrix composites . Journal of Materials Processing Technology, 210, P. 1641–1651.

8. Chornyi, M. V. (2019). Acoustic Emission Methods for Control in Technical Diagnostics. Lviv: LP Publishing [in Ukrainian].

9. Hashimoto, F. (2014). Industrial Tool Wear . CIRP Encyclopedia of Production Engineering. Springer.

10. Phadke, A. M. (1989). Quality Engineering Using Robust Design. Prentice Hall.

11. Al-Ghamd, A., & Mba, D. (2006). A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size . Mechanical Systems and Signal Processing, 20, 1537–1571.

12. Dornfeld, D. A., & Lee, D. E. (2007). Precision Manufacturing. Springer.

13. Aggelis, D. G. (2011). Classification of cracking mode in concrete by acoustic emission parameters . Mechanics Research Communications, 38(3), 153–157.

14. Holroyd, T. J. (2002). Acoustic Emission & Ultrasonic Inspection in Condition Monitoring and Acoustic Emission. Coxmoor Publishing.