Skip to main content
Log in

A New Method of Measuring the Edges of a Mill with a Shaped Cutting Surface

  • Published:
Russian Engineering Research Aims and scope

Abstract

The cutting part of mills determines their operational efficiency and the product quality. The cutting edges of mills with complex production surfaces have special design features as a result of intersection of the helical rake surfaces with the rear surface. Accordingly, in this case standart monitoring of the cutting edges’ geometry is problematic. In the present work, a method is proposed for monitoring of the cutting edges by means of machine vision. The new method is based on algorithms and models expressing the functional relations between the design parameters and the mechanism for image capture and recognition. These parameters ensure monitoring of the dimensional precision and the chipping of the mill’s edges. The algorithm optimizes the range of image capture by assessing he difference in color intensity and detects points on the wear edge within the horizontal observation regions for the boundary of the edge or chip. The measurement error was incorporated into the CAD design of mills with a shaped cutting surface. A Walter Helicheck Plus high-precision system is used for experiments and verification of the precision measurements at the end mill’s cutting edges. The proposed method is universal and can greatly simplify the measurement of multicutter mills.

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

Access this article

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.

REFERENCES

  1. Averchenkov, V.I., Dal’skii, A.M., Suslov, A.G., Nazarov, Yu.F., Poletaev, V.A., et al., Mashinostroenie. Entsiklopediya (Mechanical Engineering. Encyclopedia), Suslov, A.G., Ed., Moscow: Mashinostroenie, 2000, vol. III-3.

    Google Scholar 

  2. Pivkin, P., Minin, I., Volosova, M., et al., Image processing of advance milling cutters to automate the measurement of the geometric parameters of the cutting edge on optical measuring systems, Proc. SPIE, 2021, vol. 11914, p. 1191412. https://doi.org/10.1117/12.2605754

    Article  Google Scholar 

  3. Duvedi, R.K., Singh, M., Bedi, S., et al., Multipoint tool positioning of a toroidal end mill for five-axis machining of generalized tensor product Bézier surfaces, Int. J. Adv. Manuf. Technol., 2020, vol. 111, pp. 495–503. https://doi.org/10.1007/s00170-020-06006-1

    Article  Google Scholar 

  4. Semenchenko, I.I., Matyushin, V.M., and Sakharov, G.N., Proektirovanie metallorezhushchikh instrumentov (Design of Metal Cutting Tools), Moscow: Kniga po Trebovaniyu, 2013.

  5. Kirsanov, S.V., Grechishnikov, V.A., Grigor’ev, S.N., and Skhirtladze, A.G., Obrabotka glubokikh otverstii v mashinostroenii: Spravochnik (Machining of Deep Holes in Mechanical Engineering: Handbook), Moscow: Mashinostroenie, 2010.

  6. Malekian, M., Park, S., and Jun, M., Tool wear monitoring of micro-milling operations, J. Mater. Process. Technol., 2009, vol. 209, pp. 4903–4914. https://doi.org/10.1016/j.jmatprotec.2009.01.013

    Article  Google Scholar 

  7. Duvedi, R.K., Singh, M., Bedi, S., et al., Multipoint tool positioning of a toroidal end mill for five-axis machining of generalized tensor product Bézier surfaces, Int. J. Adv. Manuf. Technol., 2020, vol. 111, pp. 495–503. https://doi.org/10.1007/s00170-020-06006-1

    Article  Google Scholar 

  8. Wang, Y., Su, H., Dai, J., et al., A novel finite element method for the wear analysis of cemented carbide tool during high speed cutting Ti6Al4V process, Int. J. Adv. Manuf. Technol., 2019, vol. 103, pp. 2795–2807. https://doi.org/10.1007/s00170-019-03776-1

    Article  Google Scholar 

  9. Grigoriev, S.N., Volosova, M.A., Fedorov, S.V., et al., Development of DLC-coated solid SiAlON/TiN ceramic end mills for nickel alloy machining: Problems and prospects, Coatings, 2021, vol. 11, p. 532. https://doi.org/10.3390/coatings11050532

    Article  Google Scholar 

  10. Grigor’ev, S.N., Smolentsev, E.V., and Volosova, M.A., Tekhnologiya obrabotki kontsentrirovannymi potokami energii: Uchebnoe posobie (Concentrated Energy Flow Processing Technology: Manual), Staryi Oskol: TNT, 2009.

  11. Pivkin, P.M., Ershov, A.A., Grechishnikov, V.A., and Nadykto, A.B., A new method for the precise determination of rational geometric parameters of the helical groove and cutting part of high-performance tri-flute, Proc. SPIE, 2020, vol. 11540, p. 1154014. https://doi.org/10.1117/12.2574392

    Article  Google Scholar 

  12. Volosova, M.A., Grigoriev, S.N., and Ostrikov, E.A., Use of laser ablation for formation of discontinuous (discrete) wear-resistant coatings formed on solid carbide cutting tool by electron beam alloying and vacuum-arc deposition, Mech. Ind., 2016, vol. 17, no. 7, p. 720.

    Article  Google Scholar 

  13. Pivkin, P.M., Ershov, A.A., Volosova, M.A., et al., Reverse engineering of geometric models of advanced curved edge drills using optical measuring systems, Proc. SPIE, 2021, vol. 11867, p. 118670S. https://doi.org/10.1117/12.2602170

    Article  Google Scholar 

  14. Grigoriev, S.N., Volosova, M.A., Zelensky, A.A., et al., WEDM as a replacement for grinding in machining ceramic Al2O3-TiC cutting inserts, Metals, 2021, vol. 11, no. 6, p. 882. https://doi.org/10.3390/met11060882

    Article  Google Scholar 

  15. D’Addona, D.M. and Teti, R., Image data processing via neural networks for tool wear prediction, Procedia CIRP, 2013, vol. 12, pp. 252–257. https://doi.org/10.1016/j.procir.2013.09.044

    Article  Google Scholar 

  16. Voronin, V., Semenishchev, E., Zelensky, A., and Agaian, S., Quaternion-based local and global color image enhancement algorithm, Proc. SPIE, 2019, vol. 10993, p. 1099304. https://doi.org/10.1117/12.2519574

    Article  Google Scholar 

  17. Fernández-Robles, L., Azzopardi, G., Alegre, E., and Petkov, N., Machine-vision-based identification of broken inserts in edge profile milling heads, Rob. Comput.-Integr. Manuf., 2017, vol. 44, pp. 276–283. https://doi.org/10.1016/j.rcim.2016.10.004

    Article  Google Scholar 

  18. Voronin, V., Zelensky, A., and Agaian, S., 3-D block-rooting scheme with application to medical image enhancement, IEEE Access, 2021, vol. 9, pp. 3880–3893. https://doi.org/10.1109/ACCESS.2020.3047461

    Article  Google Scholar 

  19. Semenishchev, E., Voronin, V., Zelensky, A., and Shraifel, I., Algorithm for image stitching in the infrared, Proc. SPIE, 2019, vol. 11002, p. 110022H. https://doi.org/10.1117/12.2519537

    Article  Google Scholar 

  20. Palumbo, P.W., Swaminathan, P., and Srihari, S.N., Document image binarization: Evaluation of algorithms, Proc. SPIE, 1986, vol. 0697. https://doi.org/10.1117/12.976229

Download references

ACKNOWLEDGMENTS

This research was conducted on equipment at the State Engineering Center, STANKIN Moscow State Technical University.

Funding

Financial support was provided by grant of the President of the Russian Federation for state support of young Russian scientists - candidates of science (Competition-MK-2021). MK-5557.2021.4.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to P. M. Pivkin, V. A. Grechishnikov, A. A. Ershov or A. B. Nadykto.

Additional information

Translated by B. Gilbert

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pivkin, P.M., Grechishnikov, V.A., Ershov, A.A. et al. A New Method of Measuring the Edges of a Mill with a Shaped Cutting Surface. Russ. Engin. Res. 43, 355–358 (2023). https://doi.org/10.3103/S1068798X2304024X

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S1068798X2304024X

Keywords:

Navigation