The profile physical coefficient and its application for modelling the machined surface texture

Cover Page

Cite item

Abstract

Current trends in the development of mechanical engineering impose increasingly stringent requirements for the performance characteristics of manufactured goods. The main parameters characterizing the quality of a product as a whole are the physical, mechanical, and geometric indicators of the working surfaces of the compound units. In domestic practice, a machined surface is mainly characterized by a rather limited number of parameters (no more than 6), such as the average microroughness height, the microroughness height at 10 points, etc. However, their use is not enough to manufacture competitive products in the modern conditions. For example, international ISO/ASME/DIN standards include a much broader set of parameters required to accurately describe the performance properties of a surface. The paper analyzes the approaches to the formation of requirements for the microgeometry of the working surfaces of parts used in modern mechanical engineering. Based on the analysis, the author proposed and mathematically substantiated a general approach to modelling surface texture characteristics, which allows describing adequately the surface using a new parameter – the profile physical coefficient, since it is virtually impossible to directly compare the technologies developed in Russia with foreign analogues based on the current standards. First, the profile physical coefficient was determined at the section level. Next, it was decomposed into a Fourier series for the two-dimensional and three-dimensional cases. The paper presents the analysis of the new parameter applicability on the example of a product obtained by honing. The author concluded about the applicability of this parameter and the necessity to develop a comprehensive methodology based on it for evaluating the surface after machining.

About the authors

Igor N. Bobrovskij

Togliatti State University, Togliatti

Author for correspondence.
Email: bobri@yandex.ru
ORCID iD: 0000-0002-9513-7936

Doctor of Sciences (Engineering), researcher

Russian Federation

References

  1. Abramov A., Bobrovskij N.M., Nosov N.V., Tabakov V., Galyalieva K. Quasi-optimal correlation algorithm for measuring the parameters of surface microrelief. Key Engineering Materials, 2019, vol. 822, pp. 725–730. doi: 10.4028/ href='www.scientific.net/KEM.822.725' target='_blank'>www.scientific.net/KEM.822.725.
  2. Abramov A., Bobrovskij S.M., Nosov N.V., Tabakov V., Lopatina F. Method for determining texture parameters of processed precision surfaces by correlation. Key Engineering Materials, 2019, vol. 822, pp. 731–736. doi: 10.4028/ href='www.scientific.net/KEM.822.731' target='_blank'>www.scientific.net/KEM.822.731.
  3. Singh R.V., Raghav A.K. Experimental study and modelling of the effect of process parameters on surface roughness during honing process. Journal of the Institution of Engineers (India). Part PR: Production Engineering Division, 2010, vol. 90, pp. 3–7.
  4. Neagu C., Dumitrescu A. Neural networks modelling of process parameters in honing of thermal engines’ cylinders. Metalurgia International, 2008, vol. 13, no. 5, pp. 66–78.
  5. Feng C.-X.J., Yu Z.-G.S., Kingi U., Pervaiz B.M. Threefold vs. fivefold cross validation in one-hidden-layer and two-hidden-layer predictive neural network modeling of machining surface roughness data. Journal of Manufacturing Systems, 2005, vol. 24, no. 2, pp. 93–107. doi: 10.1016/S0278-6125(05)80010-X.
  6. Silva S.P., Brandao L.C., Pimenta P.R.F. Evaluation of quality of steering systems using the honing process and surface response methodology. Advanced Materials Research, 2011, vol. 223, pp. 821–825. doi: 10.4028/ href='www.scientific.net/AMR.223.821' target='_blank'>www.scientific.net/AMR.223.821.
  7. Tripathi B.N., Singh N.K., Vates U.K. Surface roughness influencing process parameters & modeling techniques for four stroke motor bike cylinder liners during honing: Review. International Journal of Mechanical and Mechatronics Engineering, 2015, vol. 15, no. 1, pp. 106–112.
  8. Paswan S.K., Bedi T.S., Singh A.K. Modeling and simulation of surface roughness in magnetorheological fluid based honing process. Wear, 2017, vol. 376-377, pp. 1207–1221. doi: 10.1016/j.wear.2016.11.025.
  9. Buj-Corral I., Álvarez-Flórez J., Domínguez-Fernández A. Acoustic emission analysis for the detection of appropriate cutting operations in honing processes. Mechanical Systems and Signal Processing, 2018, vol. 99, pp. 873–885. doi: 10.1016/j.ymssp.2017.06.039.
  10. Span J., Koshy P., Klocke F., Müller S., Coelho R. Dynamic jamming in dense suspensions: Surface finishing and edge honing applications. CIRP Annals, 2017, vol. 66, no. 1, pp. 321–324. doi: 10.1016/j.cirp.2017.04.082.
  11. Ma S., Liu Y., Wang Z., Wang Zh., Huang R., Xu J. The Effect of Honing Angle and Roughness Height on the Tribological Performance of CuNiCr Iron Liner. Metals, 2019, vol. 9, no. 5, article number 487. doi: 10.3390/met9050487.
  12. Hu Y., Meng X., Xie Y., Fan J. Mutual influence of plateau roughness and groove texture of honed surface on frictional performance of piston ring-liner system. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 2017, vol. 231, no. 7, pp. 838–859. doi: 10.1177/1350650116682161.
  13. Li B., Zhang S., Yan Z., Jiang D. Influence of edge hone radius on cutting forces, surface integrity, and surface oxidation in hard milling of AISI H13 steel. International Journal of Advanced Manufacturing Technology, 2018, vol. 95, pp. 1153–1164. doi: 10.1007/s00170-017-1292-z.
  14. Nguyen T.-T., Vu T.-C., Duong Q.-D. Multi-responses optimization of finishing honing process for surface quality and production rate. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2020, vol. 42, article number 604. doi: 10.1007/s40430-020-02690-y.
  15. Arantes L.J., Fernandes K.A., Schramm C.R., Leal J.E.S., Piratelli-Filho A., Franco S.D., Arencibia R.V. The roughness characterization in cylinders obtained by conventional and flexible honing processes. The International Journal of Advanced Manufacturing Technology, 2017, vol. 93, pp. 635–649. doi: 10.1007/s00170-017-0544-2.
  16. Buj-Corral I., Rodero-De-Lamo L., Marco-Almagro L. Use of results from honing test machines to determine roughness in industrial honing machines. Journal of Manufacturing Processes, 2017, vol. 28, pp. 60–69. doi: 10.1016/j.jmapro.2017.05.016.
  17. Yuan B., Han J., Wang D., Zhu Y., Xia L. Modeling and analysis of tooth surface roughness for internal gearing power honing gear. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2017, vol. 39, pp. 3607–3620. doi: 10.1007/s40430-017-0791-z.
  18. Kuznetsov V.P., Voropaev V.V., Skorobogatov A.S. Finishing and hardening of a flat surface ring area of a workpiece by rotary burnishing. Key Engineering Materials, 2017, vol. 743, pp. 245–247. doi: 10.4028/ href='www.scientific.net/KEM.743.245' target='_blank'>www.scientific.net/KEM.743.245.
  19. Bobrovskii I.N. How to Select the most Relevant Roughness Parameters of a Surface: Methodology Research Strategy. IOP Conference Series: Materials Science and Engineering, 2018, vol. 302, article number 012066. doi: 10.1088/1757-899X/302/1/012066.

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c)



This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies