CREATION OF AN ADAPTIVE ROBOTIC ARC SURFACING SYSTEM, DESIGNED TO RESTORE COMPLEX SPATIAL FORMS METAL PARTS

V.V. Dolinenko, E.V. Shapovalov, V.A. Kolyada, Т.G. Skuba

Èlektron. model. 2020, 42(6):56-71
https://doi.org/10.15407/emodel.42.06.056

ABSTRACT

The adaptive robotic system creation concept of difficult spatial forms metal parts restoration in which technology of an electric arc surfacing is used is offered. Arc surfacing implementation on the basis of industrial robots equipped with means of adaptation can significantly improve the quality and productivity of parts restoration while reducing the cost of energy and welding materials. The paper uses both theoretical research methods — analysis, idealization and formalization, and experimental — simulation. The solution of the repair CAD workpiece model identification problem and the installation adaptation implementation are considered. The robotic system adaptive capabilities are realized with the help of non-contact means of technical vision a triangulation laser-television sensor. The work results can be used in the areas of adaptive robotic restoration creation by electric arc surfacing in the engineering, railway and energy industries.

KEYWORDS

metal parts restoration of complex spatial forms, electric arc surfacing, robot manipulator, triangulation laser-television sensor, installation adaptation, CAD workpiece model.

REFERENCES

  1. Yeltsov, V.V. (2015). Vosstanovleniye i uprochneniye detaley mashyn [Recovering and hardening of machine parts], TGU, Tolyatti, Russia.
  2. State standart 27674-88 (1992), “Friction, wear and lubrication. Terms and definitions” from March 31, 1988, Izdatelstvo standartov, Moscow, USSR.
  3. State standart 2601-84 (1997), “Welding of metals. Terms and definitions of basic concepts” from July 01, 1985. Izdatelstvo standartov, Moscow, USSR.
  4. FARO SCANARM. URL: https://www.faro.com/russia/products/faro-scanarm.
  5. Lobanov, L.M., Shapovalov, Ye.V. and Kolyada, V.A. (2014), “Application of modern information technologies to solve problems of technological process automation”, Tekhnicheskaya diagnostika i nerazrushayushchiy control, 4, pp. 52-56.
  6. Guzhov, V.I. (2015), Metody izmereniya 3D-profilya ob’yektov. Kontaktnyye, triangulyatsionnyye sistemy i metody strukturirovannogo osveshcheniya [Methods for measuring the 3D profile of objects. Contact, triangulation systems and structured lighting methods], NGTU, Novosibirsk, Russia.
  7. MetraSCAN 750-R. URL: https://www.creaform3d.com/en/metrology-solutions/cube-r-automated-quality-control.
  8. Popov, S.B. (2013), “The use of structured lightning in computer vision systems”, Kompyuternaya optika, 37, no. 2, pp. 233-238.
    https://doi.org/10.18287/0134-2452-2013-37-2-233-238
  9. Skuba, Ò.G., Shapovalov, E.V. and Dolinenko, V.V. (2019), “Position identification in space of objects with complex geometry in ARC surfacing and NDT tasks”, Elektronne modelyuvannya, Vol. 41, no. 1, pp. 67-80.
    https://doi.org/10.15407/emodel.41.01.067
  10. Virtual Robot Experimentation Platform: User Manual. URL: http://www.coppeliarobotics.com/helpFiles.
  11. Attaway, S. (2009), Matlab: A Practical Introduction to Programming and Problem Solving, College of Engineering, Boston University, Boston, MA.
  12. State standart 6937-91 (1991), “Cone crushers. General technical requirements”, Izda­telstvo standartov, Moscow, USSR.
  13. Remondino, F. (2003), “From point cloud to surface: The modeling and visualization problem”, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIV-5/W10.

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