Skip to main content
SpringerLink
Log in

Improvement of the Quality of Cutting Tools States Recognition Using Cloud Technologies

  • Conference paper
  • First Online:
Advances in Design, Simulation and Manufacturing III (DSMIE 2020)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Included in the following conference series:

Abstract

The work considers improving the quality of constructing large-scale diagnostic models in technical diagnostics systems by developing a software architecture for high-performance computing in the form of a web service using cloud-based machine learning technologies. The obtained results are brought to practical realization in the form of tools of the automated system of technical diagnostics of cutting tools with the diagnostic parameters of large dimensions. A method has been developed for building information models of cutting tool states based on indirect measurements using test pulse effects on a cutting system in the form of loads with impacts and recording system responses, based on which information models are built in the form of multidimensional transition functions. The methods of forming test pulse loads of the cutting system by successive insertion of the cutting tool into the workpiece with different cutting depths, with variable feed, and with variable cutting duration are considered. The computational experiment demonstrates the advantages of information models in the form of multidimensional transition functions for modeling nonlinear dynamic systems in problems of diagnosing the states of cutting tools. It has been established that multiclass cutting tools state recognition can be used as an effective technology of automated technical diagnostics systems.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Fomin, O., Ruban, O., Derevyanchenko, O.: An approach to the construction of a nonlinear dynamic model process cutting for diagnosis condition of tools. Appl. Asp. Inf. Technol. 2(2), 115–126 (2019)

    Google Scholar 

  2. Fainzilberg, L.S.: Mathematical methods for assessing the utility of diagnostic features. «Osvita Ukraine», Kiyv (2010)

    Google Scholar 

  3. Fomin, O.O.: Formation of a diagnostic features space on the basis of cross sections of Volterra kernels. Math. Comput. Modeling. Tech. Sci. 17, 141–150 (2018)

    Google Scholar 

  4. Kalnauz, D., Speranskiy, V.: Productivity estimation of serverless computing. Appl. Asp. Inf. Technol. 1(2), 20–28 (2019)

    Google Scholar 

  5. Chen, T.-C.T.: Cloud intelligence in manufacturing. J. Intell. Manuf. 5(28), 1057–1059 (2017)

    Article  Google Scholar 

  6. Mukku, V.D., Lang, S., Reggelin, T.: Integration of LiFi technology in an industry 4.0 learning factory. Procedia Manuf. 31, 232–238 (2019). In: 9th Conference on Learning Factories, pp. 232–238. Braunschweig, Germany (2019)

    Article  Google Scholar 

  7. Baena, F., Guarin, A., Mora, J., Sauza, J., Retat, S.: Learning factory: the path to industry 4.0. Procedia Manuf. 9, 73–80 (2017)

    Article  Google Scholar 

  8. Malladi, A., Potluri, S.: A study on technologies in cloud-based design and manufacturing. Int. J. Mech. Prod. Eng. Res. Dev. 6(8), 187–192 (2018)

    Google Scholar 

  9. Liu, Y., Wang, L., Vincent Wang, X.: Cloud manufacturing: latest advancements and future trends. Procedia Manuf. 25, 62–73 (2018). In: 8th Swedish Production Symposium, pp. 62–73, Waterfront Convention Centre Stockholm, Sweden (2018)

    Article  Google Scholar 

  10. Newman, S.T., Nassehi, A., Xu, X.W., Rosso, R.S.U., Wang, L., Yusof, Y., et al.: Strategic advantages of interoperability for global manufacturing using CNC technology. Robot. Comput. Integr. Manuf. 24(6), 699–708 (2008)

    Article  Google Scholar 

  11. Derevyanchenko, O.G., Krinitsin, D.A.: Intelligent System for Diagnosing Failures and Predicting the Life of Cutting Tools. Astroprint, Odesa (2012). (in Russian)

    Google Scholar 

  12. Derevyanchenko, O.G., Pavlenko, V.D., Fomin, O.O., Bovnegra, L.V., Pavlenko, S.V.: Intelligent System of Cutting Tools States Recognition. Astroprint, Odesa (2013). (in Russian)

    Google Scholar 

  13. Deac, G.C., Deac, C.N., Popa, C.L., Ghinea, M., Cotet, C.E.: Machine vision in manufacturing processes and the digital twin of manufacturing architectures. In: Annals of DAAAM and Proceedings of the International DAAAM Symposium, Vienna, Austria, pp. 733–736 (2017)

    Google Scholar 

  14. Sun, W.-H., Yeh, S.-S.: Using the machine vision method to develop an on-machine insert condition monitoring system for computer numerical control turning machine tools. Materials 11(10), 2–17 (2018)

    Google Scholar 

  15. Rifai, A.P., Fukuda, R., Aoyama, H.: Image based identification of cutting tools in turning-milling machines. Jpn. Soc. Precis. Eng. 2(85), 159–166 (2019)

    Article  Google Scholar 

  16. Kumar, P., Chauhan, S.R., Pruncu, C.I., Gupta, M.K., Pimenov, D.Y., Mia, M., Gill, H.S.: Influence of different grades of CBN inserts on cutting force and surface roughness of AISI H13 die tool steel during hard turning operation. Materials 1(12), 177 (2019)

    Article  Google Scholar 

  17. Chungchoo, C., Saini, D.: On-line tool wear estimation in CNC turning operations using fuzzy neural network model. Int. J. Mach. Tools Manuf 1(42), 29–40 (2019)

    Google Scholar 

  18. Lu, Z., Ma, P., Xiao, J., Wang, M., Tang, X.: On-line monitoring of tool wear conditions in machining processes based on machine tool data. Zhongguo Jixie Gongcheng China Mech. Eng. 2(30), 220–225 (2019)

    Google Scholar 

  19. Liang, S.Y., Hecker, R.L., Landers, R.G.: Machining process monitoring and control: the state-of-the-art. J. Manuf. Sci. Eng. Trans. ASME 2(126), 297–310 (2004)

    Article  Google Scholar 

  20. Xie, Z., Li, J., Lu, Y.: Feature selection and a method to improve the performance of tool condition monitoring. Int. J. Adv. Manuf. Technol. 9–12(100), 3197–3206 (2019)

    Article  Google Scholar 

  21. Zhang, X., Tsang, W.-M., Yamazaki, K., Mori, M.: A study on automatic on-machine inspection system for 3D modeling and measurement of cutting tools. J. Intell. Manuf. 1(24), 71–86 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Odessa National Polytechnic University, 1, Shevchenko Ave., Odessa, 65044, Ukraine

    Oleksandr Fomin & Oleksandr Derevianchenko

Authors
  1. Oleksandr Fomin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oleksandr Derevianchenko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oleksandr Fomin .

Editor information

Editors and Affiliations

  1. Sumy State University, Sumy, Ukraine

    Vitalii Ivanov

  2. Poznan University of Technology, Poznan, Poland

    Justyna Trojanowska

  3. Sumy State University, Sumy, Ukraine

    Ivan Pavlenko

  4. Technical University of Kosice, Presov, Slovakia

    Jozef Zajac

  5. University of Zagreb, Zagreb, Croatia

    Dragan Peraković

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fomin, O., Derevianchenko, O. (2020). Improvement of the Quality of Cutting Tools States Recognition Using Cloud Technologies. In: Ivanov, V., Trojanowska, J., Pavlenko, I., Zajac, J., Peraković, D. (eds) Advances in Design, Simulation and Manufacturing III. DSMIE 2020. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-50794-7_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-50794-7_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50793-0

  • Online ISBN: 978-3-030-50794-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation