Designing of Optimal Grinding Cycles, Sustainable to Unstable Mechanical Processing on the Basis of Synthesis of Digital Double Technology, and Dynamic Programming Method

  • P. P. Pereverzev
  • A. V. AkintsevaEmail author
  • M. K. Alsigar
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


Currently, there are non-calculation methods of optimal grinding cycles that are resistant to unstable processing conditions for CNC machines in automated engineering; this makes technologists to lower the cutting conditions significantly to guarantee avoiding reject in grinding operations. As a result, CNC machines are used inefficiently; full automation of the preparation of control programs for CNC machines becomes impossible without using high-performance optimum grinding cycles ensuring stable processing accuracy; it is also impossible to design manufacturing cyber-physical systems in accordance with the concept of “Industry 4.0.” The article describes the synthesis of the digital twin technology and dynamic programming method for designing the optimal grinding cycle for resistance to variable technological factors, which makes it possible to: prevent the rejection of circular grinding; determine the causes of rejection; improve reliability and stability of the grinding cycle to the cumulative effect of variable factors; predict the fluctuation of accuracy and roughness parameters, hardness of the machined surface when processing a batch of parts. The practical result of the synthesis of the digital twin technology and dynamic programming method is an increase in the level of designing automation of control programs for CNC machines, ensuring the calculation of optimal values of radial feed at all cycle stages, the optimal distribution of the allowance removal over the cycle stages, which ensures the minimum main grinding cycle time and reduction in risks to meet the specified requirements on the quality of the machined surface of the part.


Dynamic programming method Digital twin Cycle Grinding 



Allowance, mm


Axial feed speed, mm/min


Dynamic programming method


Radial feed, mm/dv.stroke

\(m_{S}^{*}\) and \(m_{V}^{**}\)

The coordinate of the node from which the optimal move is made is memorized


The number of axial feed speed


The number of radial feed


The number of the allowance disk


The number of the radial feed switching stage


The number of the stage of switching the axial feed speed


  1. 1.
    Engineering industry standards of time and the cutting modes for rationing of the works carried out on the universal and multi-purpose machines with numerical program control. Part II. Standards of the cutting modes (1990) Ekonomika. MoscowGoogle Scholar
  2. 2.
    The cutting modes for the works carried out on grinding and hand-operated lapping machines and semiautomatic machines: reference book (2007). ATKOSO Publ., ChelyabinskGoogle Scholar
  3. 3.
    Cahill MJ, Bechtold MJ, Fess E, Wolfs FL, Bechtold R (2015) Ultrasonic precision optical grinding technology. In: Proceedings of SPIE—The International Society for Optical Engineering 9633:96330Google Scholar
  4. 4.
    Lur’e GB (1979) Optimizing the grinding cycle by adaptive control. Mashinostroitel’, MoscowGoogle Scholar
  5. 5.
    Dong S, Danai K, Malkin S, Deshunukh A (2004) Continuous optimal in feed control for cylindrical plunge grinding. Part 1. Methodology. J Manuf Sci Eng 126(2):327–333.
  6. 6.
    Amitay G, Malkin S, Koren Y (1981) Adaptive control optimization of grinding. J Eng Ind 103(1):103–108. Scholar
  7. 7.
    Phan AM, Summers MP, Parmigiani JP (2011) Optimization device for grinding media performance parameters. Int Mech Eng Congr Expos (IMECE) 3:915–923Google Scholar
  8. 8.
    Alagumurthi N, Panairadja K, Soundararajan V (2006) Optimization of grinding process through Design of Experiment (DOE)—a comparative study. Mater Manuf Processes 21(1):19–21CrossRefGoogle Scholar
  9. 9.
    Lee TS, Ting TP, Lin YJ, Htay T (2007) A particle swarm approach for grinding process optimization analysis. Int J Adv Manuf Technol 33(11):1128–1135. Scholar
  10. 10.
    Zhang J, Liang SY, Yao J, Chen JM, Huang JL (2006) Evolutionary optimization of machine processes. J Intell Manuf 17(2):203–215. Scholar
  11. 11.
    Bertsekas D (2005) Dynamic programming and optimal control. Athena Scientific, BelmontzbMATHGoogle Scholar
  12. 12.
    Lee CW (2009) Dynamic optimization of the grinding process in batch production. J Manuf Sci Eng, Trans ASME 131:61–66Google Scholar
  13. 13.
    Nishimura T, Inasaki I, Yamamoto N (1989) Study on optimization of internal grinding cycle. Trans Jpn Soc Mech Eng 55:1808–1813CrossRefGoogle Scholar
  14. 14.
    Inasaki I (1991) Monitoring and optimization of internal grinding process. CIRP Ann Manuf Technol 400:359–363Google Scholar
  15. 15.
    Alvarez J, Barrenetxea D, Marquinez JI, Begiaga I, Gallego I (2014) Continuous variable feed rate: a novel method for improving infeed grinding processes. Int J Adv Manuf Technol 73:53–61CrossRefGoogle Scholar
  16. 16.
    Akintseva A, Pereverzev PP (2017) Prospects for the development of the theory of designing optimal cycles of machining in a multidimensional space of control parameters. MATEC Web Conf 129:01018CrossRefGoogle Scholar
  17. 17.
    Bellman R (1960) Dynamic programming. Foreign Literature Publishing House, MoscowzbMATHGoogle Scholar
  18. 18.
    Pereverzev PP, Akintseva AV (2015) Automatic cycles’ multiparametric optimization of internal grinding. Procedia Eng 129:121–126. Scholar
  19. 19.
    Pereverzev PP, Akintseva AV (2016) Optimal internal grinding cycles in multidimensional control-parameter space. Russ Eng Res 36(11):974–978. Scholar
  20. 20.
    Akintseva AV, Pereverzev PP (2017) Complex optimization of parameters for controlling the cycle of internal grinding by the method of dynamic programming. MATEC Web Conf 129:01019CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • P. P. Pereverzev
    • 1
  • A. V. Akintseva
    • 1
    Email author
  • M. K. Alsigar
    • 2
  1. 1.South Ural State UniversityChelyabinskRussia
  2. 2.College of Engineering, University of Dhi QarNasiriyahRepublic of Iraq

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