Skip to main content
SpringerLink
Log in

Comprehensive investigation on sound generation mechanisms during machining for monitoring purpose

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Tool wear has a significant influence on machining processes. 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. Then, the relation between the sound generation mechanisms and chip formation has been studied during machining using sharp and worn tool. Hence, a deep understanding about the machining process has been brought out. Findings have led to an effective approach to monitoring the machining process, not only using mathematical signal processing methods, but also through a physical comprehension background. A healthy signal independent feature has been proposed to determine the machining condition regarding the perception about the sound generation mechanisms. Extracted features from the sound signals which have been acquired through experimental tests have shown the effectiveness of the purposed approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33
Fig. 34
Fig. 35
Fig. 36

Similar content being viewed by others

Availability of data and material

Data and materials are available and can be shared upon request.

Code availability

Any code and simulation data are available and can be shared upon request.

References

  1. Kovač P et al (2011) A review of machining monitoring systems. J Prod Eng 14(1):1–6

    Google Scholar 

  2. Lebar A et al (2010) Method for online quality monitoring of AWJ cutting by infrared thermography. CIRP J Manuf Sci Technol 2(3):170–175

    Article  Google Scholar 

  3. Nasir V, Cool J, Sassani F (2019) Intelligent machining monitoring using sound signal processed with the wavelet method and a self-organizing neural network. IEEE Robot Autom Lett 4(4):3449–3456

    Article  Google Scholar 

  4. Ahmad MAF et al (2015) Development of tool wear machining monitoring using novel statistical analysis method, I-kaz™. Procedia Eng 101:355–362

    Article  Google Scholar 

  5. Plaza EG, López PN, González EB (2019) Efficiency of vibration signal feature extraction for surface finish monitoring in CNC machining. J Manuf Process 44:145–157

    Article  Google Scholar 

  6. Inasaki I (1998) Application of acoustic emission sensor for monitoring machining processes. Ultrasonics 36(1–5):273–281

    Article  Google Scholar 

  7. Oliveira TLL et al (2020) Smart machining: Monitoring of CFRP milling using AE and IR. Compos Struct 249:112611

  8. Ong P, Lee WK, Lau RHJ (2019) Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision. Inte J Adv Manuf Technol 104(1):1369–1379

    Article  Google Scholar 

  9. Kao JY, Tarng YS (1997) A neutral-network approach for the on-line monitoring of the electrical discharge machining process. J Mater Process Technol 69(1–3):112–119

    Article  Google Scholar 

  10. Chen SL, Jen YW (2000) Data fusion neural network for tool condition monitoring in CNC milling machining. Int J Mach Tools Manuf 40(3):381–400

    Article  MathSciNet  Google Scholar 

  11. Lee WJ et al (2019) Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia Cirp 80:506–511

    Article  Google Scholar 

  12. Lee CH et al (2020) An intelligent system for grinding wheel condition monitoring based on machining sound and deep learning. IEEE Access 8:58279–58289

    Article  Google Scholar 

  13. Serin G et al (2020) Review of tool condition monitoring in machining and opportunities for deep learning. Int j Adv Manuf Technol 109(3):953–974

    Article  Google Scholar 

  14. Ahmed YS, Arif AFM, Veldhuis SC (2020) Application of the wavelet transform to acoustic emission signals for built-up edge monitoring in stainless steel machining. Measurement 154:107478

  15. Mohanraj T et al (2021) Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms. Measurement 173:108671

    Article  Google Scholar 

  16. Maged A et al (2018) Statistical monitoring and optimization of electrochemical machining using Shewhart Charts and response surface methodology. Int J Eng Mater Manuf 3(2):68–77

    Google Scholar 

  17. Komanduri R, Von Turkovich BF (1981) New observations on the mechanism of chip formation when machining titanium alloys. Wear 69(2):179–188

    Article  Google Scholar 

  18. Shaw MC, Vyas A (1998) The mechanism of chip formation with hard turning steel. CIRP Ann 47(1):77–82

    Article  Google Scholar 

  19. Che J et al (2020) Experimental and numerical studies on chip formation mechanism and working performance of the milling tool with single abrasive grain. J Pet Sci Eng 195:107645

  20. Liu Qi et al (2021) Mechanism of chip formation and surface-defects in orthogonal cutting of soft-brittle potassium dihydrogen phosphate crystals. Mater Des 198:109327

  21. Balogun VA et al (2016) Specific energy based evaluation of machining efficiency. J Clean Prod 116:187–197

    Article  Google Scholar 

  22. Guo YB, Chou YK (2004) The determination of ploughing force and its influence on material properties in metal cutting. J Mater Process Technol 148(3):368–375

    Article  Google Scholar 

  23. Ghosh S, Chattopadhyay AB, Paul S (2008) Modelling of specific energy requirement during high-efficiency deep grinding. Int J Mach Tools Manuf 48(11):1242–1253

    Article  Google Scholar 

  24. Sarwar M et al (2009) Measurement of specific cutting energy for evaluating the efficiency of bandsawing different workpiece materials. Int J Mach Tools Manuf 49(12–13):958–965

    Article  Google Scholar 

  25. Xiuli Fu et al (2018) Morphology evolution and micro-mechanism of chip formation during high-speed machining. Int J Adv Manuf Technol 98(1):165–175

    Article  Google Scholar 

  26. Prakash M, Kanthababu M, Rajurkar KP (2015) Investigations on the effects of tool wear on chip formation mechanism and chip morphology using acoustic emission signal in the microendmilling of aluminum alloy. Int J Adv Manuf Technol 77(5):1499–1511

    Article  Google Scholar 

  27. Astakhov VP (2005) On the inadequacy of the single-shear plane model of chip formation. Int J Mech Sci 47(11):1649–1672

    Article  Google Scholar 

  28. Siddhpura A, Paurobally R (2012) A study of the effects of friction on flank wear and the role of friction in tool wear monitoring. Aust J Mech Eng 10(2):141–156

    Article  Google Scholar 

  29. Guo Y, Compton WD, Chandrasekar S (2015) In situ analysis of flow dynamics and deformation fields in cutting and sliding of metals. Proc R Soc A Math Phys Eng Sci 471(2178):20150194

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran

    Reza Nourizadeh, S. Mehdi Rezaei & Hamed Adibi

  2. New Technologies Research Center, Amirkabir University of Technology, Tehran, Iran

    Mohammad Zareinejad

Authors
  1. Reza Nourizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Mehdi Rezaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Zareinejad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hamed Adibi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Mehdi Rezaei.

Ethics declarations

Ethics approval

This study does not involve human/animal participants. Therefore, ethics approval is not applicable.

Consent to participate

This study does not involve human participants. Therefore, consent to participate is not applicable.

Consent for publication

This study does not involve human participants. Therefore, consent for publication is not applicable.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nourizadeh, R., Rezaei, S.M., Zareinejad, M. et al. Comprehensive investigation on sound generation mechanisms during machining for monitoring purpose. Int J Adv Manuf Technol 121, 1589–1610 (2022). https://doi.org/10.1007/s00170-022-09333-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-09333-7

Keywords

Search

Navigation