Skip to main content
SpringerLink
Log in

Hinkley criterion applied to detection and location of burn in grinding process

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

Abstract

Grinding is a machining process widely applied in the manufacture of products that require low tolerance and high-dimensional accuracy. One of the most critical issues in this manufacturing process is the material burn phenomenon, which can lead the part to a total failure. Therefore, monitoring burn in the grinding process is crucial to ensure a high level of quality, productivity, and repeatability of industrial processes. This article presents a new non-destructive approach for the onset location of burn in the SAE 1045 steel, aiming to develop a reliable and robust grinding monitoring system as an alternative to invasive methods. An experimental investigation was conducted by using low-cost piezoelectric diaphragms and feature extraction of the signals through Hinkley criterion. By comparing to the traditional and invasive tests such as microhardness measurements and metallographic analysis, it was concluded that the Hinkley criterion has a high effectiveness and potential to locate the onset of burn, since the error of the location was less than 4%.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Zhensheng Y, Wei Y, Li J et al (2020) A novel feature representation method based on original waveforms for acoustic emission signals. Mech Syst Signal Process 135:106365

    Article  Google Scholar 

  2. Buj-Corral I, Álvarez-Flórez J, Domínguez-Fernández A (2018) Acoustic emission analysis for the detection of appropriate cutting operations in honing processes. Mech Syst Signal Process 99:873–885

    Article  Google Scholar 

  3. Tkocz J, Greenshields D, Dixon S (2019) High power phased EMAT arrays for nondestructive testing of as-cast steel. NDT E Int 102:47–55

    Article  Google Scholar 

  4. Chen Y, Chen B, Yao Y, Tan C, Feng J (2019) A spectroscopic method based on support vector machine and artificial neural network for fiber laser welding defects detection and classification. NDT E Int 108:102176

    Article  Google Scholar 

  5. Chang Y, Jiao J, Liu X, Li G, He C, Wu B (2020) Nondestructive evaluation of fatigue in ferromagnetic material using magnetic frequency mixing technology. NDT E Int 111:102209

    Article  Google Scholar 

  6. Dotto FRL, Aguiar PR, Alexandre FA, Lopes WN, Bianchi EC (2019) In-dressing acoustic map by low-cost piezoelectric transducer. IEEE Trans Ind Electron 1–1

  7. Junior POC et al (2019) Feature extraction using frequency spectrum and time domain analysis of vibration signals to monitoring advanced ceramic in grinding process. IET Sci Meas Technol 13(1):1–8 1

    Article  Google Scholar 

  8. Kwak JS, Ha MK (2004) Neural network approach for diagnosis of grinding operation by acoustic emission and power signals. J Mater Process Technol 147(1):65–71

    Article  Google Scholar 

  9. Jin T, Yi J, Peng S 2016 Determination of burn thresholds of precision gears in form grinding based on complex thermal modelling and Barkhausen noise measurements. Int J Adv Manuf Technol

  10. Yang Z, Wu H, Yu Z, Huang Y (2014) A non-destructive surface burn detection method for ferrous metals based on acoustic emission and ensemble empirical mode decomposition: from laser simulation to grinding process. Meas Sci Technol 25:035602

    Article  Google Scholar 

  11. Malkin S, Guo C (2008) Grinding technology: theory and applications of machining with abrasives. Industrial Press Inc, New York

    Google Scholar 

  12. Liang SY, Hecker RL, Landers RG (2004) Machining process monitoring and control: the state-of-the-art. J Manuf Sci Eng 126:297

    Article  Google Scholar 

  13. Ribeiro DMS, Aguiar PR, Fabiano LFG, D’Addona DM, Baptista FG, Bianchi EC (2017) Spectra measurements using piezoelectric diaphragms to detect burn in grinding process. IEEE Trans Instrum Meas 66:3052–3063

    Article  Google Scholar 

  14. Castro BA, Baptista FG, Ciampa F (2019) Comparative analysis of signal processing techniques for impedance-based SHM applications in noisy environments. Mech Syst Signal Process 126:316–340

    Article  Google Scholar 

  15. Hinkley DV (1971) Inference about the change-point from cumulative sum tests. Biometrika 58:509

    Article  MathSciNet  Google Scholar 

  16. Wang Z, Willett P, Deaguiar PR, Webster J (2001) Neural network detection of grinding burn from acoustic emission. Int. J. Mach. Tools Manuf. 41:283–309

    Article  Google Scholar 

  17. Spadotto MM, Aguiar PR, Souza CCP, Bianchi EC, de Souza AN (2008) Classification of burn degrees in grinding by neural Nets. Artif Intell Appl

  18. Gao Z, Wang X, Lin J, Liao Y (2017) Online evaluation of metal burn degrees based on acoustic emission and variational mode decomposition. Measurement 103:302–310

    Article  Google Scholar 

  19. Markalous S, Tenbohlen S, Feser K (2008) Detection and location of partial discharges in power transformers using acoustic and electromagnetic signals. IEEE Trans Dielectr Electr Insul 15:1576–1583

    Article  Google Scholar 

  20. Win SS, Aung MM, Swe W 2011 Partial discharge detection and localization in power transformers. The 8th Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology (ECTI) Association of Thailand - Conference 2011(IEEE) pp 673–6

  21. Grill BHW, Liu B, Vahab Toufigh TK (2013) Embedded piezoelectric sensors for health monitoring of concrete structures. ACI Mater J 110

  22. Kurz JH, Grosse CU, Reinhardt H-W (2005) Strategies for reliable automatic onset time picking of acoustic emissions and of ultrasound signals in concrete. Ultrasonics 43:538–546

    Article  Google Scholar 

  23. Argus P, Gurka M, Kelkel B 2019 Development of a small-scale and low-cost SHM system for thin-walled CFRP structures based on acoustic emission analysis and neural networks. Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII ed A L Gyekenyesi (SPIE) p 49

  24. Marchi M, Baptista FG, de Aguiar PR, Bianchi EC (2015) Grinding process monitoring based on electromechanical impedance measurements. Meas Sci Technol 26:45601

    Article  Google Scholar 

  25. Griffiths B (2001) The manufacturing process unit event manufacturing surface technology: surface integrity and functional performance. pp 30–69

  26. Čilliková M, Mičieta B, Neslušan M, Blažek D (2016) Nondestructive magnetic monitoring of grinding damage. Procedia Mater Sci 12:54–59

    Article  Google Scholar 

  27. Neslušan M, Čížek J, Kolařík K, Minárik P, Čilliková M, Melikhova O (2017) Monitoring of grinding burn via Barkhausen noise emission in case-hardened steel in large-bearing production. J Mater Process Technol 240:104–117

    Article  Google Scholar 

  28. Chou YK, Evans CJ (1999) White layers and thermal modeling of hard turned surfaces. Int J Mach Tools Manuf 39:1863–1881

    Article  Google Scholar 

  29. Ramesh A, Melkote SN, Allard LF, Riester L, Watkins TR (2005) Analysis of white layers formed in hard turning of AISI 52100 steel. Mater Sci Eng 390:88–97

    Article  Google Scholar 

  30. Walton IM, Stephenson DJ, Baldwin A (2006) The measurement of grinding temperatures at high specific material removal rates. Int J Mach Tools Manuf 46:1617–1625

    Article  Google Scholar 

  31. Bosheh SS, Mativenga PT (2006) White layer formation in hard turning of H13 tool steel at high cutting speeds using CBN tooling. Int J Mach Tools Manuf 46:225–233

    Article  Google Scholar 

  32. Sharman ARC, Amarasinghe A, Ridgway K (2008) Tool life and surface integrity aspects when drilling and hole making in Inconel 718 J. Mater Process Technol 200:424–432

    Article  Google Scholar 

  33. Becze CE, Clayton P, Chen L, El-Wardany TI, Elbestawi MA (2000) High-speed five-axis milling of hardened tool steel. Int J Mach Tools Manuf 40:869–885

    Article  Google Scholar 

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

    Google Scholar 

  35. Girault E, Jacques P, Harlet P, Mols K, Van Humbeeck J, Aernoudt E, Delannay F (1998) Metallographic methods for revealing the multiphase microstructure of TRIP-assisted steels. Mater Charact 40:111–118

    Article  Google Scholar 

  36. Martins CHR, Aguiar PR, Frech A, Bianchi EC (2014) Tool condition monitoring of single-point dresser using acoustic emission and neural networks models. IEEE Trans Instrum Meas 63:667–679

    Article  Google Scholar 

  37. Liu Q, Chen X, Gindy N (2006) Investigation of acoustic emission signals under a simulative environment of grinding burn. Int J Mach Tools Manuf 46:284–292

    Article  Google Scholar 

  38. Yang Z, Yu Z, Xie C, Huang Y (2014) Application of Hilbert-Huang Transform to acoustic emission signal for burn feature extraction in surface grinding process. Meas J Int Meas Confed 47:14–21

    Article  Google Scholar 

  39. IEEE Standard on piezoelectricity. 1987 An American National Standard IEEE-ANSI

  40. Liu Q, Chen X, Gindy N (2005) Fuzzy pattern recognition of AE signals for grinding burn. Int J Mach Tools Manuf 45:811–818

    Article  Google Scholar 

  41. Robles G, Fresno JM, Martínez-Tarifa JM (2015) Separation of radio-frequency sources and localization of partial discharges in noisy environments. Sensors (Switzerland) 15:9882–9898

    Article  Google Scholar 

  42. Annamdas VG, Radhika MA (2013) Electromechanical impedance of piezoelectric transducers for monitoring metallic and non-metallic structures: a review of wired, wireless and energy-harvesting methods. J Intell Mater Syst Struct 24:1021–1042

    Article  Google Scholar 

  43. Nathan RD, Vijayaraghavan L, Krishnamurthy R (1999) In-process monitoring of grinding burn in the cylindrical grinding of steel. J Mater Process Technol 37–42

  44. Del Re F, Dix M, Tagliaferri F (2019) Grinding burn on hardened steel: characterization of onset mechanisms by design of experiments. Int J Adv Manuf Technol 101:2889–2905. https://doi.org/10.1007/s00170-018-3156-6

    Article  Google Scholar 

  45. Kishawy HA, Hegab H, Umer U et al (2018) Application of acoustic emissions in machining processes: analysis and critical review. Int J Adv Manuf Technol 98:1391–1407. https://doi.org/10.1007/s00170-018-2341-y

    Article  Google Scholar 

  46. Batista da Silva R, Ferreira FI, Baptista FG, Aguiar PR, Souza RR, Hubner HB, Penha CFM, Bianchi EC (2018) Electromechanical impedance (EMI) technique as alternative to monitor workpiece surface damages after the grinding operation. Int J Adv Manuf Technol 98:2429–2438

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank NORTON of Saint Gobain Group for donating the grinding wheels. Also, thanks go to the Brazilian National Council for Scientific and Technological Development – CNPq, grant 306435/2017-9, for supporting this research.

Funding

Brazilian National Council for Scientific and Technological Development – CNPq, grant 306435/2017-9

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, School of Engineering, São Paulo State University (UNESP), Bauru, SP, 17033-360, Brazil

    Carine G. Távora, Paulo R. Aguiar, Bruno A. Castro, Felipe A. Alexandre & André L. Andreoli

  2. Department of Mechanical Engineering, School of Engineering, São Paulo State University (UNESP), Bauru, SP, 17033-360, Brazil

    Eduardo C. Bianchi

Authors
  1. Carine G. Távora
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paulo R. Aguiar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bruno A. Castro
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Felipe A. Alexandre
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. André L. Andreoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Eduardo C. Bianchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paulo R. Aguiar.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Availability of data and materials

Not applicable.

Code availability

Not applicable.

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

Távora, C.G., Aguiar, P.R., Castro, B.A. et al. Hinkley criterion applied to detection and location of burn in grinding process. Int J Adv Manuf Technol 113, 3177–3188 (2021). https://doi.org/10.1007/s00170-021-06828-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-021-06828-7

Keywords

Search

Navigation