Manufacturing Technology 2025, 25(2):222-229 | DOI: 10.21062/mft.2025.023

Diagnostics of Milling Head Using Acoustic Emission

Paweł Piórkowski ORCID..., Andrzej Roszkowski ORCID..., Zofia Szabla ORCID...
Department of Machine Tools and Mechanical Technologies, Wroclaw University of Science and Technology, Wybrzeże Stanisława Wyspiańskiego 27, 50-370 Wrocław, Poland

Monitoring and diagnostics of cutting tools are crucial for ensuring production efficiency and product quality in the machining industry. This study uses acoustic emission (AE) to non-invasively detect damage and monitor tool condition in real time. Experiments assessed cutting inserts in a milling head, both used and new. Results showed AE effectively diagnoses tool wear, with significant differences in signals from worn and new inserts. Fast Fourier Transform (FFT) analysis determined the frequency range of signals during machining, confirming AE's usefulness. Microscope verification supported the AE findings on tool wear. This research highlights AE's potential in non-destructive diagnostics, enhancing production efficiency and product quality

Keywords: Acoustic Emission (AE), Tool Wear Detection, Non-Invasive Diagnostics, Signal Analysis, Cutting Inserts

Received: July 15, 2024; Revised: March 27, 2025; Accepted: April 9, 2025; Prepublished online: April 30, 2025; Published: May 6, 2025  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Piórkowski P, Roszkowski A, Szabla Z. Diagnostics of Milling Head Using Acoustic Emission. Manufacturing Technology. 2025;25(2):222-229. doi: 10.21062/mft.2025.023.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. CIABURRO, G., & IANNACE, G. (2022). Machine-Learning-Based Methods for Acoustic Emission Testing: A Review. Applied Sciences. https://doi.org/10.3390/app122010476. Go to original source...
    2. SHAHKAR, S., & KHORASANI, K. (2019). Gas Turbine Condition Monitoring Using Acoustic Emission Signals. Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems. https://doi.org/10.1115/1.4044232 Go to original source...
    3. DUDZIK, K. (2019). The Possibility of Applying Acoustic Emission Method for Monitoring Lathing Process. Journal of KONES, 26, 21 - 27. https://doi.org/10.2478/kones-2019-0028 Go to original source...
    4. UHLMANN, E., HOLZNAGEL, T., & CLEMENS, R. (2022). Practical Approaches for Acoustic Emission Attenuation Modelling to Enable the Process Monitoring of CFRP Machining. Journal of Manufacturing and Materials Processing. https://doi.org/10.3390/jmmp6050118 Go to original source...
    5. SUN, S., HU, X., & ZHANG, W. (2020). Detection of tool breakage during milling process through acoustic emission. The International Journal of Advanced Manufacturing Technology, 109, 1409 - 1418. https://doi.org/10.1007/s00170-020-05751-7 Go to original source...
    6. ANAHID, M., HEYDARNIA, H., NIKNAM, S., & MEHMANPARAST, H. (2020). Evaluating the sensitivity of acoustic emission signal features to the variation of cutting parameters in milling aluminum alloys: Part A: frequency domain analysis. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 235, 1558 - 1568. https://doi.org/10.1177/0954405420949127 Go to original source...
    7. LI, M., HUANG, D., & YANG, X. (2021). Chatter stability prediction and detection during high-speed robotic milling process based on acoustic emission technique. The International Journal of Advanced Manufacturing Technology, 117, 1589 - 1599. https://doi.org/10.1007/s00170-021-07844-3 Go to original source...
    8. SIO-SEVER, A., LEAL-MUÑOZ, E., LÓPEZ-NAVARRO, J., ALZUGARAY-FRANZ, R., VIZAN-IDOIPE, A., & CASTRO, G. (2020). Non-Invasive Estimation of Machining Parameters during End-Milling Operations Based on Acoustic Emission. Sensors (Basel, Switzerland), 20. https://doi.org/10.3390/s20185326 Go to original source...
    9. SIO-SEVER, A., LÓPEZ-NAVARRO, J., ASENSIO-RIVERA, C., VIZAN-IDOIPE, A., & ARCAS, G. (2022). Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI Modelling. Sensors (Basel, Switzerland), 22. https://doi.org/10.3390/s22103807 Go to original source...
    10. ZHAO, L., KANG, L., & YAO, S. (2019). Research and Application of Acoustic Emission Signal Processing Technology. IEEE Access, 7, 984-993. https://doi.org/10.1109/ACCESS.2018.2886095 Go to original source...
    11. AHMED, M., KAMAL, K., RATLAMWALA, T., HUSSAIN, G., ALQAHTANI, M., ALKAHTANI, M., ALATEFI, M., & ALZABIDI, A. (2023). Tool Health Monitoring of a Milling Process Using Acoustic Emissions and a ResNet Deep Learning Model. Sensors (Basel, Switzerland), 23. https://doi.org/10.3390/s23063084 Go to original source...
    12. KUNTOĞLU, M., ASLAN, A., PIMENOV, D.Y., USCA, Ü.A., SALUR, E., GUPTA, M.K., MIKOLAJCZYK, T., GIASIN, K., KAPŁONEK, W., SHARMA, S. (2021). "A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends". Sensors, 21, 108. https://doi.org/10.3390/s21010108 Go to original source...
    13. XU, Y., GUI, L., XIE, T. (2021). "Intelligent Recognition Method of Turning Tool Wear State Based on Information Fusion Technology and BP Neural Network". Shock and Vibration, vol. 2021, Article ID 7610884, 10 pages. https://doi.org/10.1155/2021/7610884 Go to original source...
    14. DUDZIK, K., LABUDA, W. (2019). "The Possibility of Applying Acoustic Emission and Dynamometric Methods for Monitoring the Turning Process". Materials, 12, 2926. https://doi.org/10.3390/ma13132926 Go to original source...
    15. PAPANDREA, P.J., FRIGIERI, E.P., MAIA, P.R., OLIVEIRA, L.G., PAIVA, A.P. (2017). "Surface roughness diagnosis in hard turning using acoustic signals and support vector machine: A PCA-based approach". Procedia CIRP, 63, 348-353. https://doi.org/10.1016/j.procir.2017.03.173 Go to original source...
    16. KUSTERNIG, F., BLIEBERGER, J. (2021). "A Survey on Noise and Vibrations in Machine Tools". CIRP Annals - Manufacturing Technology, 70(2), 611-633. https://doi.org/10.1016/j.cirp.2021.05.010 Go to original source...
    17. YAMAN, K., BAŞALTIN, M. (2017). "Investigations on the cutting parameters and the tool wear of SAE 1030 forged steel material by acoustic emission in turning operation". Journal of the Faculty of Engineering and Architecture of Gazi University, Vol. 32, No. 4, 1077-1088. https://doi.org/10.17341/gazimmfd.369394 Go to original source...
    18. WICIAK-PIKUŁA, M., FELUSIAK-CZYRYCA, A., TWARDOWSKI, P. (2020). "Tool Wear Prediction Based on Artificial Neural Network during Aluminum Matrix Composite Milling". Sensors, 20, 5798. https://doi.org/10.3390/s20205798 Go to original source...
    19. QI, Y., XU, J., YU, Z., & YU, H. (2017). Acoustic emission monitoring in high-speed micro end-milling based on SVD-EEMD method. 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), 1154-1159. https://doi.org/10.1109/ROBIO.2017.8324573 Go to original source...
    20. PECHENIN, V., KHAIMOVICH, A., KONDRATIEV, A., & BOLOTOV, M. (2017). Method of Controlling Cutting Tool Wear Based on Signal Analysis of Acoustic Emission for Milling. Procedia Engineering, 176, 246-252. https://doi.org/10.1016/J.PROENG.2017.02.294 Go to original source...
    21. KAROLCZAK, P. (2023). Analysis of Cutting Forces with Application of the Discrete Wavelet Transform in Titanium Ti6Al4V Turning. Manufacturing Technology, 23(4), 449-452. https://doi.org/10.21062/mft.2023.062 Go to original source...
    22. SLABEJOVÁ, S., HOLUBJAK, J., TIMKO, P., RICHTÁRIK, M., KRAJČOVIECH, S., & PROKEIN, D. (2022). Cutting Forces in the Milling of Difficult-to-Machine Material Used in the Aero Space Industry Using a Monolithic Ceramic Milling Cutter. Manufacturing Technology, 22(2), 211-213. https://doi.org/10.21062/mft.2022.019 Go to original source...
    23. SAŁAMACHA, D., & JÓZWIK, J. (2023). Evaluation of Measurement Uncertainty Obtained With a Tool Probe on a CNC Machine Tool. Manufacturing Technology, 23(4), 513-515. https://doi.org/10.21062/mft.2023.051 Go to original source...
    24. PAWLIK, P. (2023). The acoustic method for diagnosing machines operating under variable conditions. INTER-NOISE and NOISE-CON Congress and Conference Proceedings. https://doi.org/10.3397/in_2023_0844. Go to original source...
    25. ADIN, V., ZHANG, Y., OELMANN, B., & BADER, S. (2023). Tiny Machine Learning for Damage Classification in Concrete Using Acoustic Emission Signals. 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 1-6. https://doi.org/10.1109/I2MTC53148.2023.10175972. Go to original source...
    26. SHUBITA, R., ALSADEH, A., & KHATER, I. (2023). Fault Detection in Rotating Machinery Based on Sound Signal Using Edge Machine Learning. IEEE Access, 11, 6665-6672. https://doi.org/10.1109/ACCESS.2023.3237074 Go to original source...

    This is an open access article distributed under the terms of the Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content