Development of a method for constructing a 3D image of the surface structure of parts based on their profilograms

This work is aimed at obtaining 3D images of the microrelief of working surfaces of machine parts using a profilograph, which represents a simple and convenient approach to analyzing the surface structure. To obtain information about the 3D distribution of height parameters of a microrelief, its structural parameters, rather than microgeometry profile, should be used. To build a 3D model of the microrelief of precision parts, a profilogram obtained by conventional GOST methods is used. The digital values of a signal are entered into a computer in the form of a one-dimensionalarray (the number of elements of this array will determine the size of the generated image). In the process of modeling, new requirements for the microgeometry characteristics of working surfaces are formulated. It is proposed to consider the one-dimensional array as a single random realization of a video camera signal along the X axis. Then its replication along the Y axis will form a 3D model of the examined surface. In order to eliminate the drawback of inadequate imaging of the 3D model of the studied surface, it is proposed to introduce a random component using a pseudorandom number generator to add noise to each successive line of the image under construction. This generator is implemented in the C++ programming language. It was established that video signal graphs for different lines differ significantly, thereby reflecting the real microrelief structure. The proposed method for constructing digital images can be used to obtain a 3 D model of the surface structure under examination for further processing of these signals by an optical electronic method, without using complex and expensive equipment.

1. Suslov A.G. The quality of machine part surface layer. Moscow: Mashinostroenie; 2000, 320 p. (In Russ.).

2. Klevtsov G.V., Frolovа O.A., Klevtsova N.A. The effect of surface treatment on the material microrelief and structural changes in the surface layer. Fundamental’niye issledovaniya = Fundamental research. 2005;4:71-73. (In Russ.).

3. Prihod'ko V.M., Medelyaev I.A., Fatyuhin D.S. Formation of machine part operational properties by ultrasonic methods: monograph. Moscow: Moscow Automobile and Road Construction State Technical University; 2015, 264 p. (In Russ.).

4. Abramov A.D. Estimation of machine part surface microrelief parameters based on quasi-optimal correlation algorithms. Vestnik komp'iuternykh i informatsionnykh tekhnologii = Herald of Computer and Information Technologies . 2016;9:19-25. (In Russ.).

5. Abramov A.D., Grishin R.G., Nosov N.V. Electrooptic estimation of texture parameters of precision surfaces. Journal of Physics: Conference Series. The IV International Conference on Information Technology and Nanotechnology . 2018;1096:168-175. https://doi.org/10.1088/1742-6596/1096/1/012021.

6. Nosov N.V., Mikhailova L.N. Research surface roughness of the tapered roller bearing. Izvestiya Samarskogo nauchnogo tsentra Rossiiskoi akademii nauk = Izvestia of Samara Scientific Center of the Russian Academy of Sciences. 2018;20(4-2):232-237. (In Russ.).

7. Zibrov P.F., Bobrovskij I.N., Bobrovskij N.M. Profile study of a rough layer with microroughnesses limited by a complex curve consisting of concave and convex semicircles in a two-dimensional space. STIN. 2020;6:27-30. (In Russ.).

8. Bobrovskij I., Khaimovich A., Bobrovskij N., D’yakonov A. Determination of wide burnishing energy-force parameters based on constructing the kinematically admissible velocity field. Metals. 2020;10(1):46. https://doi.org/10.3390/met10010046.

9. Nоsоv N.V., Kоstin N.A., Ladyagin R.V. Estimation of texture parameters for the precision surfaces using the quasioptimal correlation algorithms. Vektor nauki Tol'yattinskogo gosudarstvennogo universiteta = Science Vector of Togliatti State University. 2021;1:24-31. (In Russ.). https://doi.org/10.18323/2073-5073-2021-1-24-31.

10. Abramov A.D., Nikonov A.I. Analysis and correlation method for eliminating the errors of opto-electronic determination of microrelief parameters. Vestnik komp'iuternykh i informatsionnykh tekhnologii = Herald of Computer and Information Technologies. 2016;1:3-9. (In Russ.).

11. Abramov A.D., Nosov N.V., Kostin N.A. Analysis of surface profilogram structural parameters in optical-electronic systems. In: Vysokie tekhnologii v mashinostroenii: materialy XIX Vserossijskoj nauchno-tekhnicheskoj konferencii s mezhdunarodnym uchastiem = High technologies in mechanical engineering: materials of the 19th All-Russian scientific and technical conference with international participation. 10–11 November 2022, Samara: Samara Polytech Flagship University; 2022, р. 3-5. (In Russ.).

12. Ivankin V.Yu., Pepelyaeva T.F. Surface design technique based on specified roughness parameters. Perspektivy nauki = Science Prospects. 2014;3:73-75. (In Russ.).

13. Nosov N.V., Yakubovich E.A. Research of the surface quality of the compressor blades feather after diamond vibrocontact polishing. Journal of Advanced Research in Technical Science. 2021;26:27-31. (In Russ.).

14. Smirnov A.V., Bezzubcev A.Yu. Bypass obstacles mobile technical unit using stereo vision. Programmnye sistemy: teoriya i prilozheniya = Program systems: Theory and applications. 2016;7(4):331-346. (In Russ.).

15. Milanich A.I., Baranov A.A. Limit resolution in optics. Trudy Moskovskogo fiziko-tekhnicheskogo instituta. 2012;4(2):177-181. (In Russ.).

16. Azarova V.V., Chertovich I.V., Cvetkova T.V. Interferometric monitoring method for precision surfaces and laser mirrors. In: Trudy I Vserossijskoj shkoly-seminara. 1–3 December 2010, Moscow. Moscow: HSE Tikhonov Moscow Institute of Electronics and Mathematics; 2010, р. 209-214. (In Russ.).

17. Milanich A.I. Monochrome microscope of ultra-high resolution. Patent RF, no. 2441291; 2012. (In Russ.).

18. Libenson M.N. Overcoming diffraction limit in optics. Sorosovskij obrazovatel'nyj zhurnal = Soros Educational Journal. 2000;6(3):99-104. (In Russ.).

19. Keler A., Bredski G. Izuchaem OpenCV 3. Learning OpenCV 3 Computer Vision in C++ with the OpenCV Library, 2017, 826 р. (Russ. ed.: Razrabotka programm komp'yuternogo zreniya na C++ s primeneniem biblioteki OpenCV . Moscow, DMK Press; 2017, 826 р.)

20. Abramov A.D., Nikonov A.I. Optoelectronic systems and estimation methods of microrelief parameters. Samara: Samara Polytech Flagship University; 2012, 207 р. (In Russ.).