Skip to main content
SpringerLink
Log in

Investigations on the effects of tool wear on chip formation mechanism and chip morphology using acoustic emission signal in the microendmilling of aluminum alloy

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

Abstract

This work investigates the effects of tool wear on surface roughness (Ra), chip formation mechanisms and chip morphology in the microendmilling of aluminum alloy (AA 1100) using acoustic emission (AE) signals. The acquired AE signals are analysed in the time domain, frequency domain using fast Fourier transformation (FFT) and the discrete wavelet transformation (DWT) technique. The time domain analysis indicates that the root mean square of the AE (AERMS) signals is sensitive to the formation of the buildup edge apart from effective machining. The frequency domain analysis indicates that the dominant frequency of the AE signals lies between 150 and 300 kHz. The AE-specific energies are computed by decomposing the AE signals in different frequency bands, using the DWT technique. The higher and lower orders of AE-specific energies are obtained. The higher order of AE-specific energies indicates chip formation mechanisms such as shearing and microfracture. Chip morphology studies are carried out using the FFT analysis. The FFT indicates that low-frequency and low-amplitude AE lead to tight curl chips, while high-frequency and high-amplitude AE lead to elemental/short comma chips. This work provides new significant inferences on tool wear, chip formation mechanisms and chip morphology in the microendmilling of AA 1100.

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

Similar content being viewed by others

References

  1. Mahalik NP (2007) Micromanufacturing and nanotechnology. Springer, India

    Google Scholar 

  2. Donfeld D, Min S, Takeuchi Y (2006) Recent advances in mechanical micromachining. Ann CIRP 55(2):745–768

    Article  Google Scholar 

  3. Alting L, Kimura F, Hansen HN, Bissacco G (2003) Micro engineering. CIRP Ann Manuf Technol 52(2):635–657

    Article  Google Scholar 

  4. Camara MA, Campos Rubio JC, Abrao AM, Davim JP (2012) State of the art of micromilling of materials, a review. J Mater Sci Technol 28(8):673–685

    Article  Google Scholar 

  5. Bissacco G, Hansen HN, De Chiffre L (2005) Micromilling of hardened tool steel for mould making applications. J Mater Process Technol 167:201–207

    Article  Google Scholar 

  6. Asad ABMA, Masaki T, Rahman M, Lim HS, Wong YS (2007) Tool-based micro-machining. J Mater Process Technol 192–193:204–211

    Article  Google Scholar 

  7. Filiz S, Conley CM, Wasserman MB, Ozdoganlar OB (2007) An experimental investigation of micro-machinability of copper 101 using tungsten carbide micro-endmills. Int J Mach Tools Manuf 47:1088–1100

    Article  Google Scholar 

  8. Takacs M, Vero B, Meszaros I (2003) Micromilling of metallic materials. J Mater Process Technol 138:152–155

    Article  Google Scholar 

  9. Dolinsek S, Kopac J (1999) Acoustic emission signals for tool wear identification. Wear 225–229:295–303

    Article  Google Scholar 

  10. Haber RE et al (2004) An investigation of tool-wear monitoring in a high-speed machining process. Sensors Actuators A 116:539–545

    Article  Google Scholar 

  11. Malekian M, Park SS, Jun MBG (2009) Tool wear monitoring of micro-milling operations. J Mater Process Technol 209:4903–4914

    Article  Google Scholar 

  12. Gandarias E, Dimov S, Pham DT, Ivanov A, Popov K, Lizarralde R, Arrazola PJ (2006) New methods for tool failure detection in micromilling. Proc IME B J Eng Manufact 220:137–144

    Article  Google Scholar 

  13. Rangwala S, Dornfeld D (1991) A study of acoustic emission generated during orthogonal metal cutting −1: energy analysis. Int J Mech Sci 33:471–487

    Article  Google Scholar 

  14. Kanthababu M, Shunmugam MS, Singaperumal M (2012) Multi-sensor-based condition monitoring for honing of cylinder liners. Int J Manuf Res 7(4):376–396

    Article  Google Scholar 

  15. Jemielniak K, Arrazola PJ (2008) Application of AE and cutting force signals in tool- condition monitoring in micro-milling. CIRP J Manuf Sci Technol 1:97–102

    Article  Google Scholar 

  16. Kanthababu M, Shunmugam MS, Singaperumal M (2008) Tool condition monitoring in honing process using acoustic emission signals. Int J Autom Control 2(1):99–112

    Article  Google Scholar 

  17. Teti R, Jemielniak K, Donnell O, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Technol 59:717–739

    Article  Google Scholar 

  18. Mathew M, Pai PS, Rocha LA (2008) An effective sensor for tool wear monitoring in face milling: acoustic emission. Sadhana 33(3):227–233

    Article  Google Scholar 

  19. Chen X, Li B (2007) Acoustic emission method for tool condition monitoring based on wavelet analysis. Int J Adv Manuf Technol 33:968–976

    Article  Google Scholar 

  20. Papacharalampopoulos A, Stavropoulos P, Doukas C, Foteinopoulos P, Chryssolouris G (2013) Acoustic emission signal through turning tools: a computional study. Procedia CIRP 8:425–430, 14th CIRP Conference on Modeling of Machining Operations (CIRP CMMO)

  21. Xiaoli L, Zhejun Y (1998) Tool wear monitoring with wavelet packet transform-fuzzy clustering method. Wear 219:145–154

    Article  Google Scholar 

  22. Vidyasagar R (2009) An experimental study on acoustic emission energy and fracture energy of concrete. Proceedings of the National Seminar & Exhibition on Non-Destructive Evaluation NDE 2009, December 10–12

  23. Kluft W, Konig W, Luttervelt CA (1979) Present knowledge of chip control. Ann CIRP 28(2):441–445

    Google Scholar 

  24. Barry J, Byne G, Lennon G (2001) Observations on chip formation and acoustic emission in machining Ti6Al4V alloy. Int J Mach Tools Manuf 41:1055–1070

    Article  Google Scholar 

  25. Kim CJ, Mayor JR, Ni J (2004) A static model of chip formation in microscale milling. Trans ASME 126:710–718

    Google Scholar 

  26. Simoneau A, Ng E, Elbestawi MA (2006) Chip formation during microscale cutting of carbon steel. Int J Mach Tools Manuf 46:467–481

    Article  Google Scholar 

  27. Segreto T, Simeone A, Teti R (2012) Chip form classification in carbon steel turning through cutting force measurement and principal component analysis. Procedia CIRP 2:49–54, 1st CIRP Global Web Conference: Interdisciplinary Research in Production Engineering (CIRPE2012)

  28. Prakash M, Kanthababu M (2013) In-process tool condition monitoring using acoustic emission sensor in microendmilling. Int J Mach Sci Technol 17(2):209–227

    Article  Google Scholar 

  29. Jackson MJ (2005) Primary chip formation during the micromachining of engineering materials. ProQuest Sci J 219–245

  30. Lee DE, Hwang I, Valente CMO, Oliveira JFG, Dornfeld DA (2006) Precision manufacturing process monitoring with acoustic emission. Int J Mach Tools Manuf 46(2):176–188

    Article  Google Scholar 

  31. Mian AJ, Driver N, Mativenga PT (2011) Chip formation in microscale milling and correlation with acoustic emission signal. Int J Adv Manuf Technol 56:63–78

    Article  Google Scholar 

  32. Mian AJ, Driver N, Mativenga PT (2011) Identification of factors that dominate size effect in micro-machining. Int J Mach Tools Manuf 51:383–394

    Article  Google Scholar 

  33. Prakash M, Kanthababu M, Moorthy TV (2010) Effect of microendmilling parameters on surface roughness in steel alloy. Proceedings of 3rd International and 24th AIMTDR Conference, Vishakhapatanam.

  34. Nakamoto K, Katahira K, Ohmori H, Yamazaki K, Aoyama T (2012) A study on the quality of micro-machined surfaces on tungsten carbide generated by PCD micro end-milling. CIRP Ann Manuf Technol 61:567–570

    Article  Google Scholar 

  35. Kuram E, Ozcelik B (2013) Multi-objective optimization using Taquchi based grey relational analysis for micro-milling of Al 7075 material with bass nose end mill. J Meas 46:1849–1864

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Manufacturing Engineering, College of Engineering Guindy, Anna University, Chennai, 600 025, India

    M. Prakash & M. Kanthababu

  2. Center for Non-Traditional Manufacturing Research, Industrial and Management System Engineering, University of Nebraska–Lincoln, Lincoln, NE, 68588-0518, USA

    K. P. Rajurkar

Authors
  1. M. Prakash
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Kanthababu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. P. Rajurkar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Kanthababu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prakash, M., Kanthababu, M. & Rajurkar, K.P. 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, 1499–1511 (2015). https://doi.org/10.1007/s00170-014-6562-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-6562-4

Keywords

Search

Navigation