Sustainability improvement of WEDM process by analysing and classifying wire rupture using kernel-based naive Bayes classifier

Abstract

The current work aims to improve the sustainability of wire electric discharge machining by predicting the wire breakages. Wire breakages are process interruptions which increase the machining time, energy wastage and material consumption. The study is a novel approach to predict process continuity by binomial classification of machining outcomes using kernel-based naive Bayes algorithm. The two classes are labelled as wire breakages and continuous machining. Training dataset consists of 31 experiments according to central composite design of response surface methodology, and wire breakage instances are recorded as response. The input dataset contains four machining parameters, namely pulse on time, pulse off time, servo voltage and wire feed rate, whereas mean gap voltage variation is derived from in-process data. The trained model was 96.7% accurate in wire breakage predictions. Further, nine confirmation tests were conducted to check model adequacy in real-world situations. The model predicted all instances of wire breakages accurately. The stages of wire wear up to wire rupture were studied by conducting microstructural analysis.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. 1.

    Ho KH, Newman ST, Rahimifard S, Allen RD (2004) State of the art in wire electrical discharge machining (WEDM). Int J Mach Tools Manuf 44:1247–1259. https://doi.org/10.1016/j.ijmachtools.2004.04.017

    Article  Google Scholar 

  2. 2.

    Abhilash PM, Chakradhar D (2020) Surface integrity comparison of wire electric discharge machined Inconel 718 surfaces at different machining stabilities. Procedia CIRP 87:228–233. https://doi.org/10.1016/j.procir.2020.02.037

    Article  Google Scholar 

  3. 3.

    Camposeco-Negrete C (2019) Prediction and optimization of machining time and surface roughness of AISI O1 tool steel in wire-cut EDM using robust design and desirability approach. Int J Adv Manuf Technol 103:2411–2422. https://doi.org/10.1007/s00170-019-03720-3

    Article  Google Scholar 

  4. 4.

    Gamage JR, Desilva AKM (2016) Effect of wire breakage on the process energy utilisation of EDM. Procedia CIRP 42:586–590. https://doi.org/10.1016/j.procir.2016.02.264

    Article  Google Scholar 

  5. 5.

    Pramanik A, Basak AK (2018) Sustainability in wire electrical discharge machining of titanium alloy: understanding wire rupture. J Clean Prod 198:472–479

    Article  Google Scholar 

  6. 6.

    Rajurkar KP, Wang WM (1991) On-line monitor and control for wire breakage in WEDM. Ann CIRP 40:219–222. https://doi.org/10.1016/j.procir.2017.12.059

    Article  Google Scholar 

  7. 7.

    Rajurkar KP, Wang WM, McGeough JA (1994) WEDM identification and adaptive control for variable-height components. CIRP Ann Manuf Technol 43:199–202. https://doi.org/10.1016/S0007-8506(07)62195-7

    Article  Google Scholar 

  8. 8.

    Yan MT, Liao YS (1996) A self-learning fuzzy controller for wire rupture prevention in WEDM. Int J Adv Manuf Technol 11:267–275. https://doi.org/10.1007/BF01351284

    Article  Google Scholar 

  9. 9.

    Kao J, Tarng Y (1997) A neutral-network approach for the on-line monitoring of the electrical discharge machining process. J Mater Process Technol 69:112–119. https://doi.org/10.1016/S0924-0136(97)00004-6

    Article  Google Scholar 

  10. 10.

    Liao YS, Chu YY, Yan MT (1997) Study of wire breaking process and monitoring of WEDM. Int J Mach Tools Manuf 37:555–567. https://doi.org/10.1016/S0890-6955(95)00049-6

    Article  Google Scholar 

  11. 11.

    Zhang X, Liu Y, Wu X, Niu Z (2020) Intelligent pulse analysis of high-speed electrical discharge machining using different RNNs. J Intell Manuf 31:937–951. https://doi.org/10.1007/s10845-019-01487-8

    Article  Google Scholar 

  12. 12.

    Rajeswari R, Shunmugam MS (2019) Investigations into process mechanics of rough and finish die sinking EDM using pulse train analysis. Int J Adv Manuf Technol 100:1945–1964. https://doi.org/10.1007/s00170-018-2701-7

    Article  Google Scholar 

  13. 13.

    Kwon S, Yang MY (2006) The benefits of using instantaneous energy to monitor the transient state of the wire EDM process. Int J Adv Manuf Technol 27:930–938. https://doi.org/10.1007/s00170-004-2252-y

    Article  Google Scholar 

  14. 14.

    Cabanes I, Portillo E, Marcos M, Sánchez JA (2008) An industrial application for on-line detection of instability and wire breakage in wire EDM. J Mater Process Technol 195:101–109. https://doi.org/10.1016/j.jmatprotec.2007.04.125

    Article  Google Scholar 

  15. 15.

    Caggiano A, Teti R, Perez R, Xirouchakis P (2015) Wire EDM monitoring for zero-defect manufacturing based on advanced sensor signal processing. Procedia CIRP 33:315–320. https://doi.org/10.1016/j.procir.2015.06.065

    Article  Google Scholar 

  16. 16.

    Oßwald K, Lochmahr I, Schulze H-P, Kröning O (2018) Automated analysis of pulse types in high speed wire EDM. Procedia CIRP 68:796–801. https://doi.org/10.1016/j.procir.2017.12.157

    Article  Google Scholar 

  17. 17.

    Caggiano A, Napolitano F, Teti R et al (2020) Advanced die sinking EDM process monitoring based on anomaly detection for online identification of improper process conditions. Procedia CIRP 88:381–386. https://doi.org/10.1016/j.procir.2020.05.066

    Article  Google Scholar 

  18. 18.

    Mwangi JW, Bui VD, Thüsing K et al (2020) Characterization of the arcing phenomenon in micro-EDM and its effect on key mechanical properties of medical-grade Nitinol. J Mater Process Technol 275:116334. https://doi.org/10.1016/j.jmatprotec.2019.116334

    Article  Google Scholar 

  19. 19.

    Xia W, Li Z, Zhang Y, Zhao W (2020) Breakout detection for fast EDM drilling by classification of machining state graphs. Int J Adv Manuf Technol 106:1645–1656. https://doi.org/10.1007/s00170-019-04530-3

    Article  Google Scholar 

  20. 20.

    Bishop CM (2006) Pattern recognition and machine learning. Springer, Berlin

    Google Scholar 

  21. 21.

    Duda RO, Hart PE, Stork DG (2012) Pattern classification. Wiley, New York

    Google Scholar 

  22. 22.

    Zhang H (2005) Exploring conditions for the optimality of naïve bayes. Int J Pattern Recognit Artif Intell 19:183–198. https://doi.org/10.1142/S0218001405003983

    Article  Google Scholar 

  23. 23.

    Karandikar J, McLeay T, Turner S, Schmitz T (2015) Tool wear monitoring using naïve Bayes classifiers. Int J Adv Manuf Technol 77:1613–1626. https://doi.org/10.1007/s00170-014-6560-6

    Article  Google Scholar 

  24. 24.

    Sharma RK, Sugumaran V, Kumar H, Amarnath M (2015) A comparative study of naive Bayes classifier and Bayes net classifier for fault diagnosis of roller bearing using sound signal. Int J Decis Support Syst 1:115. https://doi.org/10.1504/ijdss.2015.067279

    Article  Google Scholar 

  25. 25.

    Elangovan M, Ramachandran KI, Sugumaran V (2010) Studies on Bayes classifier for condition monitoring of single point carbide tipped tool based on statistical and histogram features. Expert Syst Appl 37:2059–2065. https://doi.org/10.1016/j.eswa.2009.06.103

    Article  Google Scholar 

  26. 26.

    Bhargavi P, Jyothi S (2009) Applying naive Bayes data mining technique for classification of agricultural land soils. Int J Comput Sci Netw Secur 9(8):117–122

    Google Scholar 

  27. 27.

    Stern M, Beck J, Woolf BP (1999) Naive Bayes classifiers for user modeling. Center for Knowledge Communication, Computer Science Department, University of Massachusetts, Massachusetts

    Google Scholar 

  28. 28.

    Witten IH, Frank E (2002) Data mining: practical machine learning tools and techniques with Java implementations. Acm Sigmod Record 31(1):76–77

    Article  Google Scholar 

  29. 29.

    Thakur DG, Ramamoorthy B, Vijayaraghavan L (2009) Study on the machinability characteristics of superalloy Inconel 718 during high speed turning. Mater Des 30:1718–1725. https://doi.org/10.1016/j.matdes.2008.07.011

    Article  Google Scholar 

  30. 30.

    Reed RC (2006) The superalloys fundamentals and applications. Cambridge University Press, Cambridge

  31. 31.

    Abhilash PM, Chakradhar D (2020) Prediction and analysis of process failures by ANN classification during wire-EDM of Inconel 718. Adv Manuf 8:519–536. https://doi.org/10.1007/s40436-020-00327-w

    Article  Google Scholar 

  32. 32.

    Bergmeir C, Costantini M, Benítez JM (2014) On the usefulness of cross-validation for directional forecast evaluation. Comput Stat Data Anal 76:132–143. https://doi.org/10.1016/j.csda.2014.02.001

    MathSciNet  Article  MATH  Google Scholar 

  33. 33.

    Jadam T, Datta S, Masanta M (2019) Study of surface integrity and machining performance during main/rough cut and trim/finish cut mode of WEDM on Ti–6Al–4V: effects of wire material. J Braz Soc Mech Sci Eng 41:151. https://doi.org/10.1007/s40430-019-1656-4

    Article  Google Scholar 

  34. 34.

    Mendes LA, Amorim FL, Weingaertner WL (2014) Automated system for the measurement of spark current and electric voltage in wire EDM performance. J Braz Soc Mech Sci Eng 37:123–131. https://doi.org/10.1007/s40430-014-0171-x

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the Central Instrumentation Facility (CIF), Indian Institute of Technology, Palakkad, for providing the test facilities.

Funding

No funding was received.

Author information

Affiliations

Authors

Corresponding author

Correspondence to P. M. Abhilash.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

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

Technical Editor: Izabel Fernanda Machado.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Abhilash, P.M., Chakradhar, D. Sustainability improvement of WEDM process by analysing and classifying wire rupture using kernel-based naive Bayes classifier. J Braz. Soc. Mech. Sci. Eng. 43, 64 (2021). https://doi.org/10.1007/s40430-021-02805-z

Download citation

Keywords

  • Wire EDM
  • Sustainability
  • Naive Bayes
  • Wire rupture
  • Mean gap voltage
  • Process interruption