Volume 22, Issue 8 (August 2022)
Modares Mechanical Engineering 2022, 22(8): 529-539 |
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Nourizadeh R, Zareinejad M, Rezaei S M, Adibi H. Modeling of Sound Generation Mechanism During the Turning Process. Modares Mechanical Engineering 2022; 22 (8) :529-539
URL: http://mme.modares.ac.ir/article-15-57660-en.html
URL: http://mme.modares.ac.ir/article-15-57660-en.html
1- Amirkabir University of Technology
2- Amirkabir University Of Technology , smrezaei@aut.ac.ir
2- Amirkabir University Of Technology , smrezaei@aut.ac.ir
Abstract: (1555 Views)
Tool wear has a significant influence on the turning process. Investigations on tool wear monitoring through various methods and sensors have been widely conducted to determine and predict the tool wear. In this study sound generation mechanisms during turning process have been investigated comprehensively and three sound generation sources have been determined and distinguished. Sound generation mechanisms which originated from tool vibration, deformation in the workpiece and vibration at the contact zones (friction), have been investigated and frequency range of the sound generated through each mechanism has been determined. It has been shown that these mechanisms produce sound in 10s hertz, kilohertz and megahertz respectively. Then the mechanism which is appropriate for tool condition monitoring has been studied and suggested. Then the relation between the sound generation mechanisms and chip formation has been studied during machining. Hence, a deep understanding about the machining process has been brought out. Findings could lead to an effective approach to monitoring the machining process, not only using mathematical signal processing methods, but also through a physical comprehension background. Experimental studies have been conducted to evaluate developed theories and models. Experimental results have shown effectiveness of the proposed approach.
Article Type: Original Research |
Subject:
Mechatronics
Received: 2021/12/5 | Accepted: 2022/04/18 | Published: 2022/08/1
Received: 2021/12/5 | Accepted: 2022/04/18 | Published: 2022/08/1
References
1. [1] Kovač, P., et al. (2011). "A review of machining monitoring systems." Journal of production engineering14(1): 1-6.
2. [2] WU, D. (2018). Research on Thermal Monitoring for Finish Machining of Near Net Shape Parts, (Muroran Institute of Technology).
3. [3] Nasir, V., et al. (2019). "Intelligent machining monitoring using sound signal processed with the wavelet method and a self-organizing neural network." IEEE Robotics and Automation Letters4(4): 3449-3456. [DOI:10.1109/LRA.2019.2926666]
4. [4] Plaza, E. G., et al. (2019). "Efficiency of vibration signal feature extraction for surface finish monitoring in CNC machining." Journal of Manufacturing Processes44: 145-157. [DOI:10.1016/j.jmapro.2019.05.046]
5. [5] Ahmad, M., et al. (2015). "Development of tool wear machining monitoring using novel statistical analysis method, I-kaz™." Procedia Engineering101: 355-362. [DOI:10.1016/j.proeng.2015.02.043]
6. [6] Inasaki, I. (1998). "Application of acoustic emission sensor for monitoring machining processes." Ultrasonics36(1-5): 273-281. [DOI:10.1016/S0041-624X(97)00052-8]
7. [7] Oliveira, T. L. L., et al. (2020). "Smart machining: Monitoring of CFRP Milling using AE and IR." Composite Structures249: 112611. [DOI:10.1016/j.compstruct.2020.112611]
8. [8] Bombiński, S., Kossakowska, J., & Jemielniak, K. (2022). Detection of accelerated tool wear in turning. Mechanical Systems and Signal Processing, 162, 108021. [DOI:10.1016/j.ymssp.2021.108021]
9. [8] Ong, P., et al. (2019). "Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision." The International Journal of Advanced Manufacturing Technology104(1): 1369-1379. [DOI:10.1007/s00170-019-04020-6]
10. [10] Lee, W. J., et al. (2019). "Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data." Procedia Cirp80: 506-511. [DOI:10.1016/j.procir.2018.12.019]
11. [11] Chen, S.-L. and Y. Jen (2000). "Data fusion neural network for tool condition monitoring in CNC milling machining." International journal of machine tools and manufacture40(3): 381-400. [DOI:10.1016/S0890-6955(99)00066-8]
12. [12] Kao, J. and Y. Tarng (1997). "A neural-network approach for the on-line monitoring of the electrical discharge machining process." Journal of Materials Processing Technology69(1-3): 112-119. [DOI:10.1016/S0924-0136(97)00004-6]
13. [13] Serin, G., et al. (2020). "Review of tool condition monitoring in machining and opportunities for deep learning." The International Journal of Advanced Manufacturing Technology: 1-22.
14. [14] Lee, C.-H., et al. (2020). "An intelligent system for grinding wheel condition monitoring based on machining sound and deep learning." IEEE Access8: 58279-58289. [DOI:10.1109/ACCESS.2020.2982800]
15. [15] Ahmed, Y. S., et al. (2020). "Application of the wavelet transform to acoustic emission signals for built-up edge monitoring in stainless steel machining." Measurement154: 107478. [DOI:10.1016/j.measurement.2020.107478]
16. [16] Mohanraj, T., et al. (2020). "Development of tool condition monitoring system in end milling process using wavelet features and Hoelder's exponent with machine learning algorithms." Measurement: 108671. [DOI:10.1016/j.measurement.2020.108671]
17. [17] Maged, A., et al. (2018). "Statistical monitoring and optimization of electrochemical machining using Shewhart Charts and response surface methodology." International Journal of Engineering Materials and Manufacture3(2): 68-77. [DOI:10.26776/ijemm.03.02.2018.01]
18. [18] Alonso, F. J., & Salgado, D. R. (2008). Analysis of the structure of vibration signals for tool wear detection. Mechanical systems and signal processing, 22(3), 735-748. [DOI:10.1016/j.ymssp.2007.09.012]
19. [19] Komanduri, R. and B. Von Turkovich (1981). "New observations on the mechanism of chip formation when machining titanium alloys." Wear69(2): 179-188. [DOI:10.1016/0043-1648(81)90242-8]
20. [20] Shaw, M. and A. Vyas (1998). "The mechanism of chip formation with hard turning steel." CIRP Annals47(1): 77-82. [DOI:10.1016/S0007-8506(07)62789-9]
21. [21] Che, J., et al. (2020). "Experimental and numerical studies on chip formation mechanism and working performance of the milling tool with single abrasive grain." Journal of Petroleum Science and Engineering195: 107645. [DOI:10.1016/j.petrol.2020.107645]
22. [22] Liu, Q., et al. (2021). "Mechanism of chip formation and surface-defects in orthogonal cutting of soft-brittle potassium dihydrogen phosphate crystals." Materials & Design198: 109327. [DOI:10.1016/j.matdes.2020.109327]
23. [23] Siddhpura, A. and R. Paurobally (2012). "A study of the effects of friction on flank wear and the role of friction in tool wear monitoring." Australian Journal of Mechanical Engineering10(2): 141-156. [DOI:10.7158/M12-027.2012.10.2]
24. [24] Guo, Y., et al. (2015). "In situ analysis of flow dynamics and deformation fields in cutting and sliding of metals." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences471(2178): 20150194. [DOI:10.1098/rspa.2015.0194]
25. [25] Kuntoğlu, M., & Sağlam, H. (2019). Investigation of progressive tool wear for determining of optimized machining parameters in turning. Measurement, 140, 427-436. [DOI:10.1016/j.measurement.2019.04.022]
26. [26] Priarone, P. C., Robiglio, M., Settineri, L., & Tebaldo, V. (2014). Milling and turning of titanium aluminides by using minimum quantity lubrication. Procedia Cirp, 24, 62-67. [DOI:10.1016/j.procir.2014.07.147]
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