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Advances in Computational Intelligence in Materials Science

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1st International Conference on Emerging Trends in Mechanical Sciences for Sustainable Technologies

Monitoring Tool Condition with Acoustic and Vibration Signals Using IoT

Ganesh Kumar S and Manoj S, Department of Mechanical Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India.

Online First : 07 June 2023
Publisher Name : AnaPub Publications, Kenya.
ISSN (Online) : 2960-2408
ISSN (Print) (Online) : 2960-2394
ISBN (Online) : 978-9914-9946-6-7
ISBN (Print) : 978-9914-9946-7-4
Pages : 061-069

DOI
https://doi.org/10.53759/acims/978-9914-9946-6-7_8
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Abstract

This article reviews the recent advances in the field of monitoring tool condition using acoustic and pulse signals. Specifically, the research focuses on the development and application of signal processing techniques to extract valuable information from acoustic and pulse signals generated by cutting tools during machining operations. To this end, the literature is reviewed with emphasis on the signal acquisition and analysis techniques used in the field. Additionally, the article presents a comprehensive overview of the existing methods and techniques used to monitor tool conditions, including signal analysis techniques, feature extraction techniques, and classification techniques. Furthermore, the article discusses the challenges associated with acoustic and pulse-based TCM, including signal noise and impurities, signal acquisition, feature extraction, and classification. The review concludes with a discussion of the possible future directions in the field. The use of acoustic and pulse signals to monitor the condition of cutting tools has become increasingly popular in recent years. In order to extract useful information from the signals generated by cutting tools, sophisticated signal processing techniques are required. In this article, a comprehensive review of the existing methods and techniques used to monitor tool conditions is presented. Various signal acquisition and analysis techniques are discussed, as well as feature extraction and classification methods. Additionally, the article delves into the challenges associated with acoustic and pulse.

Keywords

Acoustic and Pulse Signals, TCM, Signal Processing, Signal Analysis, Feature Extraction, Classification, Signal Acquisition, Signal Noise and Impurities.

  1. B. Denkena, M.-A. Dittrich, S. Stamm, M. Wichmann, and S. Wilmsmeier, “Reprint of: Gentelligent processes in biologically inspired manufacturing,” CIRP Journal of Manufacturing Science and Technology, vol. 34, pp. 105–118, Aug. 2021, doi: 10.1016/j.cirpj.2021.06.006.
  2. G. Aruquipa and F. Diaz, “An IoT architecture based on the control of Bio Inspired manufacturing system for the detection of anomalies with vibration sensors,” Procedia Computer Science, vol. 200, pp. 438–450, 2022, doi: 10.1016/j.procs.2022.01.242.
  3. C. Cooper et al., “Convolutional neural network-based tool condition monitoring in vertical milling operations using acoustic signals,” Procedia Manufacturing, vol. 49, pp. 105–111, 2020, doi: 10.1016/j.promfg.2020.07.004.
  4. C. Cheng, J. Li, Y. Liu, M. Nie, and W. Wang, “Deep convolutional neural network-based in-process tool condition monitoring in abrasive belt grinding,” Computers in Industry, vol. 106, pp. 1–13, Apr. 2019, doi: 10.1016/j.compind.2018.12.002.
  5. Z. Huang, J. Zhu, J. Lei, X. Li, and F. Tian, “Tool Wear Monitoring with Vibration Signals Based on Short-Time Fourier Transform and Deep Convolutional Neural Network in Milling,” Mathematical Problems in Engineering, vol. 2021, pp. 1–14, Jun. 2021, doi: 10.1155/2021/9976939.
  6. Z. Huang, J. Zhu, J. Lei, X. Li, and F. Tian, “Tool Wear Predicting Based on Multisensory Raw Signals Fusion by Reshaped Time Series Convolutional Neural Network in Manufacturing,” IEEE Access, vol. 7, pp. 178640–178651, 2019, doi: 10.1109/access.2019.2958330.
  7. X.-C. Cao, B.-Q. Chen, B. Yao, and W.-P. He, “Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification,” Computers in Industry, vol. 106, pp. 71–84, Apr. 2019, doi: 10.1016/j.compind.2018.12.018.
  8. D. Wu, C. Jennings, J. Terpenny, S. Kumara, and R. X. Gao, “Cloud-Based Parallel Machine Learning for Tool Wear Prediction,” Journal of Manufacturing Science and Engineering, vol. 140, no. 4, Feb. 2018, doi: 10.1115/1.4038002.
  9. Z. Li, X. Liu, A. Incecik, M. K. Gupta, G. M. Królczyk, and P. Gardoni, “A novel ensemble deep learning model for cutting tool wear monitoring using audio sensors,” Journal of Manufacturing Processes, vol. 79, pp. 233–249, Jul. 2022, doi: 10.1016/j.jmapro.2022.04.066.
  10. K. Balachandar and R. Jegadeeshwaran, “Friction stir welding tool condition monitoring using vibration signals and Random forest algorithm – A Machine learning approach,” Materials Today: Proceedings, vol. 46, pp. 1174–1180, 2021, doi: 10.1016/j.matpr.2021.02.061.
  11. B. Lutz et al., “Benchmark of Automated Machine Learning with State-of-the-Art Image Segmentation Algorithms for Tool Condition Monitoring,” Procedia Manufacturing, vol. 51, pp. 215–221, 2020, doi: 10.1016/j.promfg.2020.10.031.
  12. Q. Nazir and C. Shao, “Online tool condition monitoring for ultrasonic metal welding via sensor fusion and machine learning,” Journal of Manufacturing Processes, vol. 62, pp. 806–816, Feb. 2021, doi: 10.1016/j.jmapro.2020.12.050.
  13. Y. Zhou, B. Sun, and W. Sun, “A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling,” Measurement, vol. 166, p. 108186, Dec. 2020, doi: 10.1016/j.measurement.2020.108186.
  14. J. Sossenheimer, J. Walther, J. Fleddermann, and E. Abele, “A Sensor Reduced Machine Learning Approach for Condition-based Energy Monitoring for Machine Tools,” Procedia CIRP, vol. 81, pp. 570–575, 2019, doi: 10.1016/j.procir.2019.03.157.
  15. A. Caggiano, R. Angelone, F. Napolitano, L. Nele, and R. Teti, “Dimensionality Reduction of Sensorial Features by Principal Component Analysis for ANN Machine Learning in Tool Condition Monitoring of CFRP Drilling,” Procedia CIRP, vol. 78, pp. 307–312, 2018, doi: 10.1016/j.procir.2018.09.072.
  16. A. D. Patange and R. Jegadeeshwaran, “Review on tool condition classification in milling: A machine learning approach,” Materials Today: Proceedings, vol. 46, pp. 1106–1115, 2021, doi: 10.1016/j.matpr.2021.01.523.
  17. F. Jin, Y. Bao, B. Li, and X. Jin, “Tool wear prediction in edge trimming of carbon fiber reinforced polymer using machine learning with instantaneous parameters,” Journal of Manufacturing Processes, vol. 82, pp. 277–295, Oct. 2022, doi: 10.1016/j.jmapro.2022.08.006.
  18. K. Zhu, H. Guo, S. Li, and X. Lin, “Online tool wear monitoring by super-resolution based machine vision,” Computers in Industry, vol. 144, p. 103782, Jan. 2023, doi: 10.1016/j.compind.2022.103782.
  19. K. A. Ajayram, R. Jegadeeshwaran, G. Sakthivel, R. Sivakumar, and A. D. Patange, “Condition monitoring of carbide and non-carbide coated tool insert using decision tree and random tree – A statistical learning,” Materials Today: Proceedings, vol. 46, pp. 1201–1209, 2021, doi: 10.1016/j.matpr.2021.02.065.
  20. Y. Liu, L. Guo, H. Gao, Z. You, Y. Ye, and B. Zhang, “Machine vision based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: A review,” Mechanical Systems and Signal Processing, vol. 164, p. 108068, Feb. 2022, doi: 10.1016/j.ymssp.2021.108068.
  21. P. Twardowski, M. Tabaszewski, M. Wiciak – Pikuła, and A. Felusiak-Czyryca, “Identification of tool wear using acoustic emission signal and machine learning methods,” Precision Engineering, vol. 72, pp. 738–744, Nov. 2021, doi: 10.1016/j.precisioneng.2021.07.019.
  22. A. Fertig, M. Weigold, and Y. Chen, “Machine Learning based quality prediction for milling processes using internal machine tool data,” Advances in Industrial and Manufacturing Engineering, vol. 4, p. 100074, May 2022, doi: 10.1016/j.aime.2022.100074.
  23. V. Nasir, S. Dibaji, K. Alaswad, and J. Cool, “Tool wear monitoring by ensemble learning and sensor fusion using power, sound, vibration, and AE signals,” Manufacturing Letters, vol. 30, pp. 32–38, Oct. 2021, doi: 10.1016/j.mfglet.2021.10.002.
  24. H.-C. Möhring, S. Eschelbacher, and P. Georgi, “Machine learning approaches for real-time monitoring and evaluation of surface roughness using a sensory milling tool,” Procedia CIRP, vol. 102, pp. 264–269, 2021, doi: 10.1016/j.procir.2021.09.045.
  25. M. P. Motta, C. Pelaingre, A. Delamézière, L. B. Ayed, and C. Barlier, “Machine learning models for surface roughness monitoring in machining operations,” Procedia CIRP, vol. 108, pp. 710–715, 2022, doi: 10.1016/j.procir.2022.03.110.
  26. G. Gokilakrishnan, S. Ganeshkumar, H. Anandakumar, and M. Vigneshkumar, “A Critical Review of Production Distribution Planning Models,” 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), Mar. 2021, doi: 10.1109/icaccs51430.2021.9441879.

Cite this article

Ganesh Kumar S and Manoj S, “Monitoring Tool Condition with Acoustic and Vibration Signals Using IoT”, Advances in Computational Intelligence in Materials Science, pp. 061-069, June. 2023. doi:10.53759/acims/978-9914-9946-6-7_8

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© 2023 Ganesh Kumar S and Manoj S. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                           

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