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Naïve Bayes and Bayes Net Classifier for Fault Diagnosis of End Mill Tool Using Wavelet Analysis: A Comparative Study

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Journal of Vibration Engineering & Technologies Aims and scope Submit manuscript

Abstract

Purpose

In today’s manufacturing era cutting tool plays an essential role, the presence of wear in cutting tool affects machining product hence continuous tool condition monitoring overcome this problem. In this present research work, condition monitoring of the multipoint end mill tool has been carried out in each condition.

Methods

The vibration signatures were obtained in each state of the end mill tool. A wavelet feature has been extracted from these vibration signatures. The decision tree was adopted to choose the best wavelet features. The different wavelet families such as Haar wavelet, Discrete Mayer wavelet, Daubechies wavelets, Bi-orthogonal wavelets, Reversed Bi-orthogonal wavelets, Coiflets wavelets, and symlets wavelets classification results were compared with Naïve Bayes and Bayes Net classifier.

Results

The symlets wavelet family with sym5 and sym7 wavelet was best among all wavelet families. In both Naïve Bayes and Bayes net classifiers, the Symlets wavelet family gives maximum classification accuracy. In Naïve Bayes classifier with sym5 wavelet gives 98.57% and in Bayes net classifier 96.57% maximum classification.

Conclusion

For fault diagnosis of the end milling tool, it can be suggested that sym5 wavelet with Naïve Bayes classifier is best for the analysis. Hence in industries, it can be used for preventive maintenance analysis.

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References

  1. Yesilyurt I, Ozturk H (2017) Tool condition monitoring in milling using vibration analysis. Int J Prod Res 45(4):1013–1028

    Article  Google Scholar 

  2. Cho S, Binsaeid S, Asfour S (2009) Design of multisensor fusion-based tool condition monitoring system in end milling. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-009-2110-z

    Article  Google Scholar 

  3. Kaya B, Oysu C, Ertunc H et al (2012) A support vector machine-based online tool condition monitoring for milling using sensor fusion and a genetic algorithm. Proc Inst Mech Eng Part B J Eng Manuf 226(11):1808–1818. https://doi.org/10.1177/0954405412458047

    Article  Google Scholar 

  4. Zhang K, Nie HYP (2015) A method for tool condition monitoring based on sensor fusion. J Intell Manuf 26(5):1011–1026

    Article  Google Scholar 

  5. Madhusudana CK, Kumar H, Narendranath S (2016) Condition monitoring of face milling tool using K-star algorithm and histogram features of vibration signal. Eng Sci Technol Int J. https://doi.org/10.1016/j.jestch.2016.05.009

    Article  Google Scholar 

  6. Madhusudana CK, Kumar H, Narendranath S (2017) Face milling tool condition monitoring using sound signal. Int J Syst Assur Eng Manag 8:1643–1653. https://doi.org/10.1007/s13198-017-0637-1

    Article  Google Scholar 

  7. Cirp P, Stief P, Dantan J, Etienne A, Siadat A (2018) A multi-sensor based online tool condition monitoring system for milling process. Procedia CIRP 72(6):1136–1141. https://doi.org/10.1016/j.procir.2018.03.092

    Article  Google Scholar 

  8. Krishnakumar P, Rameshkumar K, Ramachandran KI (2018) Machine learning based tool condition classification using acoustic emission and vibration data in high speed milling process using wavelet features. IntelligentDecision Technologies 12(1):265–282. https://doi.org/10.3233/IDT-180332

  9. Laddada S, Si-Chaib MO, Benkedjouh T, Drai R (2020) Tool wear condition monitoring based on wavelet transform and improved extreme learning machine. Proc Inst Mech Eng Part C J Mech Eng Sci 234(5):1057–1068. https://doi.org/10.1177/0954406219888544

    Article  Google Scholar 

  10. Sun S, Hu X, Zhang W (2020) Detection of tool breakage during milling process through acoustic emission. Int J Adv Manuf Technol 109(5–6):1409–1418. https://doi.org/10.1007/s00170-020-05751-7

    Article  Google Scholar 

  11. Zacharia K, Krishnakumar P (2020) Chatter prediction in high speed machining of titanium alloy (Ti-6Al-4V) using machine learning techniques. Mater Today Proc 24:350–358. https://doi.org/10.1016/j.matpr.2020.04.286

    Article  Google Scholar 

  12. Zhou Y, Sun B, Sun W, Lei Z (2020) Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process. J Intell Manuf. https://doi.org/10.1007/s10845-020-01663-1

    Article  Google Scholar 

  13. Dhanush Abhijit V, Sugumaran V, Ramachandran KI (2016) Fault diagnosis of bearings using vibration signals and wavelets. Indian J Sci Technol 9(33):1–8. https://doi.org/10.17485/ijst/2016/v9i33/101325

    Article  Google Scholar 

  14. Pawar N, Sugumaran V, Singh A, Amarnath M (2016) Fault diagnosis of helical gear box using vibration signals through J-48 Graft algorithm and wavelet features. Indian J Sci Technol 9(47):1–8. https://doi.org/10.17485/ijst/2016/v9i47/107917

    Article  Google Scholar 

  15. Muralidharan V, Sugumaran V, Indira V (2014) Fault diagnosis of monoblock centrifugal pump using SVM. Eng Sci Technol Int J 17:152–157. https://doi.org/10.1016/j.jestch.2014.04.005

    Article  Google Scholar 

  16. Muralidharan V, Sugumaran V (2013) Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump. Measurement 46:353–359. https://doi.org/10.1016/j.measurement.2012.07.007

    Article  Google Scholar 

  17. Muralidharan V, Sugumaran V (2011) Wavelet decomposition and support vector machine for fault diagnosis of monoblock centrifugal pump. Int J Data Anal Tech Strateg 3(2):159–177

    Article  Google Scholar 

  18. Muralidharan V, Sugumaran V (2013) Selection of discrete wavelets for fault diagnosis of monoblock centrifugal pump using the j48 algorithm. Appl Artif Intell 27:1–19

    Article  Google Scholar 

  19. Madhusudana CK, Kumar H, Narendranath S (2018) Fault diagnosis of face milling tool using decision tree and sound signal. Mater Today Proc 5(5):12035–12044. https://doi.org/10.1016/j.matpr.2018.02.178

    Article  Google Scholar 

  20. Sharma RK, Sugumaran V, Kumar H, Amarnath M (2017) Condition monitoring of roller bearing by K-star classifier and K-nearest neighborhood classifier using sound signal. Struct Durab Health Monitor 12(1):1–16

    Google Scholar 

  21. Gangadhar N, Kumar H, Narendranath S, Sugumaran V (2014) Fault diagnosis of single point cutting tool through vibration signal using decision tree algorithm. Proc Mater Sci 5:1434–1441

    Article  Google Scholar 

  22. Krishnamurthy B, Sugumaran V, Jegadeeshwaran R, Muniyappa A (2015) Fault diagnosis of bearing using sound signals through histogram features and decision tree. Int J Appl Eng Res 10(68):476–481

    Google Scholar 

  23. Muralidharan V, Sugumaran V (2012) A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Appl Soft Comput 12:2023–2029

    Article  Google Scholar 

  24. Sharma RK, Sugumaran V (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(1):115–129

    Article  Google Scholar 

  25. Gangadhar, N., Vernekar, K., Kumar, H., Narendranath, S. (2017). Fault diagnosis of single point cutting tool through discrete wavelet features of vibration signals using decision tree technique and multilayer perceptron. J Vib Eng Technol 5(1), 35–44. http://idr.nitk.ac.in/jspui/handle/123456789/11293

  26. Niu B, Sun J, Yang B (2020) Multisensory based tool wear monitoring for practical applications in milling of titanium alloy. Mater Today Proc 22(1):1209–1217. https://doi.org/10.1016/j.matpr.2019.12.126

    Article  Google Scholar 

  27. 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

    Article  Google Scholar 

  28. Madhusudana CK, Budati S, Gangadhar N (2016) Fault diagnosis studies of face milling cutter using machine learning approach. J Low Freq Noise Vib Active Control 35(2):128–138. https://doi.org/10.1177/0263092316644090

    Article  Google Scholar 

  29. Yang B, Guo K, Liu J, Sun J, Song G (2020) Vibration singularity analysis for milling tool condition monitoring. Int J Mech Sci 166(116):105254. https://doi.org/10.1016/j.ijmecsci.2019.105254

    Article  Google Scholar 

  30. Madhusudana C, Gangadhar N, Kumar H, Narendranath S (2018) Use of discrete wavelet features and support vector machine for fault diagnosis of face milling tool. Strut Durab Health Monit 12(2):111–127

    Google Scholar 

  31. Gangadhar N et al (2018) Condition monitoring of single point cutting tools based on machine learning approach. Int J Acoust Vib 23:131–137

    Google Scholar 

  32. Zhou C, Guo K, Sun J, Yang B, Liu J et al (2020) Tool condition monitoring in milling using a force singularity analysis approach. Int J Adv Manuf Technol 107(3–4):1785–1792. https://doi.org/10.1007/s00170-019-04664-4

    Article  Google Scholar 

  33. Xie Z, Li J, Yong L (2019) Feature selection and a method to improve the performance of tool condition monitoring. Int J Adv Manuf Technol 100(2):3197–3206

    Article  Google Scholar 

  34. Wang G, Yang Y, Xie Q, Zang Y (2014) Force based tool wear monitoring system for milling process based on relevance vector machine. Adv Eng Softw 71:46–51

    Article  Google Scholar 

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Funding

In this research no funding was received from any funding agency.

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Authors and Affiliations

  1. G. H. Raisoni College of Engineering and Management, Pune affiliated to Savartibai Phule Pune University, Gat No.1200, Pune-Nagar Road, Wagholi, Pune, Maharashtra, 412207, India

    Nilesh Dhobale

  2. Mechanical Engineering, RMD Sinhgad School of Engineering, Bypass Pune-Mumbai Highway, Warje, Pune, Maharashtra, 411058, India

    Sharad S. Mulik

  3. Mechanical Engineering, Government College of Engineering, Vidyanagar, Karad, Satara, Karad, Maharashtra, 415124, India

    Suhas P. Deshmukh

Authors
  1. Nilesh Dhobale
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  2. Sharad S. Mulik
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  3. Suhas P. Deshmukh
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Contributions

ND: Data curation, Investigation, Writing—Original draft preparation. SM: Conceptualization, Methodology, Validation, Supervision. SD: Visualization, Writing—Reviewing and Editing.

Corresponding author

Correspondence to Sharad S. Mulik.

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Dhobale, N., Mulik, S.S. & Deshmukh, S.P. Naïve Bayes and Bayes Net Classifier for Fault Diagnosis of End Mill Tool Using Wavelet Analysis: A Comparative Study. J. Vib. Eng. Technol. 10, 1721–1735 (2022). https://doi.org/10.1007/s42417-022-00478-z

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  • DOI: https://doi.org/10.1007/s42417-022-00478-z

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