Advertisement

Predictive Modeling of Design Innovative Solutions on Tooling Configurations at High-Tech Manufacturing Companies

  • S. Lukina
  • M. Kosov
  • I. Tolkacheva
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

The article considers the method for making up a set of predictive models for design innovative solutions forming and selection in the area of the high-tech production tools configuration. The model set structuring is represented by a system of multilayer graph models combining input information forming; generation of new product properties; idea synthesis and analysis; configuration of forming operation tooling; forming of a set of technical, economic, and manufacturing criteria. The set of possible shaping systems is a function of the set of traversal routes, which in their turn are generated by consecutive inter-layer transitions from input edges to edges describing the optimal tooling configurations by means of targeted structural and parametric synthesis. The authors developed the mathematical model for forming a set of configurations for the production operation tooling. The problem of the optimum innovative solution selection is solved by means of the Boolean algorithm for linear programming. The research practical tests were performed by means of the technological synthesis problem solution for the multiaxial laser cutting machine.

Keywords

Innovative solution Predictive modeling Mathematical model Graph model Optimization 

References

  1. 1.
    Lukina SV (2016) Efficiency of technological modernization of the enterprises. Stankoinstrument 3:18–26Google Scholar
  2. 2.
    Lukina SV (2015) The technique of optimizing of the production activity of industrial plant on the basis of a complex of predictive models of formation and selection of design innovative solutions in the field of hightech industries. Vestn MSTU Stankin 1:125–129Google Scholar
  3. 3.
    Kłos S, Patalas-Maliszewska J (2015) Throughput analysis of automatic production lines based on simulation methods. In: Jackowski K, Burduk R, Walkowiak K, Wozniak M, Yin H (eds) Intelligent data engineering and automated learning-IDEAL, vol. 9375. Lecture notes in computer science. Springer, Cham, pp 181–190CrossRefGoogle Scholar
  4. 4.
    Kosov MG (2014) Approaches to judgment of the concept “Technical”. Teh tvorc molod 6:25–28Google Scholar
  5. 5.
    Khmelevskaya SA, Kosov MG, Volkova GD (2014) The nature of theoretical abstractions in science: problem of their objectivization. Alma mater (Vestn vyss shkoly) 4:9–12Google Scholar
  6. 6.
    Trojanowska J, Kolinski A, Galusik D, Varela M, Machado J (2017) A methodology of improvement of manufacturing productivity through increasing operational efficiency of the production process. Adv Manuf 22–32.  https://doi.org/10.1007/978-3-319-68619-6-3
  7. 7.
    Starzyńska B, Hamrol A (2013) Excellence toolbox: decision support system for quality tools and techniques selection and application. Total Qual Manag Bus Excell 24:577–595.  https://doi.org/10.1080/14783363.2012.669557CrossRefGoogle Scholar
  8. 8.
    Makarov VM, Lukina SV (2013) The knowledge-intensive engineering in problems of technical re-equipment. RITM Remont Innov Tehnol Mod 8:16–20Google Scholar
  9. 9.
    Makarov VM, Lukina SV (2014) Management of changes of products of dual purpose. RITM Remont Innov Tehnol Mod 4:22–27Google Scholar
  10. 10.
    Wheaton J (1999) The non predictive part of predictive modeling. Cat Age 12:127–128Google Scholar
  11. 11.
    Rogalewicz M, Sika R (2016) Methodologies of knowledge discovery from data and data mining methods in mechanical engineering. Manag Prod Eng Rev 4:97–108.  https://doi.org/10.1515/mper-2016-0040CrossRefGoogle Scholar
  12. 12.
    Lukina SV (2009) Modeling procedures for formation and choice of structural component layout of modular cutting tools using network graph-models. Met Work Mater Sci 2:28–30Google Scholar
  13. 13.
    Lukina SV (2011) Autmating procedures for formation and choice of structural component layout of modular cutting tools in step of technical preparation production. Vestn Saratov State Tech Univ 1:241–247Google Scholar
  14. 14.
    Lukina S (2015) Formation of the system of the local indicators to assess the quality of the cutting tool at the stage of technical training of production. Met Work Mater Sci 4:43–50.  https://doi.org/10.17212/1994-6309-2015-4-43-50CrossRefGoogle Scholar
  15. 15.
    Bekaev AA, Maksimov YV, Lukina SV (2015) Predicting the surface quality in discontinuous cutting. Russ Eng Res 10:792–794.  https://doi.org/10.3103/s1068798x15100044CrossRefGoogle Scholar
  16. 16.
    Asanov RE, Kosov MG, Kuznetsov AP (2013) Assessment of the technical level of mechatronic products. Vestn MSTU Stankin 1:60–65Google Scholar
  17. 17.
    Tolkacheva IM, Lukina SV, Kosov MG, Tolkachev OI (2013) Structural and parametrical and information model of technological preparation of production. Janus-K, MoscowGoogle Scholar
  18. 18.
    Adlemam L, Booth KS, Preparata FP, Ruzzo WL (1978) Improved time and space bounds for boolean multiplication. Acta Inform 11:61–199MathSciNetzbMATHGoogle Scholar
  19. 19.
    Bryant RE (1992) Simbolic Boolean manipulation with ordered binary-decision diagrams. ACM Comput Surv 3:293–318CrossRefGoogle Scholar
  20. 20.
    Mason P (1986) Computerized cutting-tool management. Am Mach Autom Manuf 5(106–113):116–120Google Scholar
  21. 21.
    Fraassen BC (1980) The scientific image. Oxford University Press, OxfordCrossRefGoogle Scholar
  22. 22.
    Mello CHP, Turrioni JB, Xavier AF, Campos DF (2012) Action research in industrial engineering: design organization proposal for its application. Prod J 1:1–13.  https://doi.org/10.1590/s0103-65132011005000056CrossRefGoogle Scholar
  23. 23.
    Nowicki P, Kafel P, Sikora T (2013) Selected requirements of integrated management systems based on PAS 99 specification. Int J Qual Res 1:97–106Google Scholar
  24. 24.
    Lukina S, Manaenkov I (2017) Methodology of multiaxial machines formats volumetric accuracy comparative evaluation. MATEC Web Conf 129.  https://doi.org/10.1051/matecconf/201712901046CrossRefGoogle Scholar
  25. 25.
    Grechishnikov VA, Lukina SV, Veselov AI, Makarov DV (2001) The forming processes modeling for geometric parameters of inserted cutting tools with regard to their assembly technology. Avtom Sovr Tekhnol 4:32–34Google Scholar
  26. 26.
    Lukina SV (2015) Development of a predictive model complex for generating and choosing innovative design solutions in the field of hi-tech production. Actual Probl Mach Build 2:451–456Google Scholar
  27. 27.
    Lukina SV, Ivannikov SN, Krutjakova MV, Manaenkov IV (2013) Technological synthesis of mechatronic machine-tool systems for multi-axis machining. Izv MSTU “MAMI” 1:146–151Google Scholar
  28. 28.
    Lukina SV, Ivannikov SN, Manaenkov IV (2013) Method of formation and selection of the optimal configuration of forming system for multi-coordinate processing. Izv MSTU “MAMI” 2:237–242Google Scholar
  29. 29.
    Lukina SV, Kudryavtseva AL, Manaenkov IV (2013) Technological synthesis of the multiaxial machine for laser processing. RITM Remont Innov Tehnol Mod 1:36–40Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Moscow State University of Tehnology “STANKIN”MoscowRussia
  2. 2.College of Automation and Information TechnologyMoscowRussia

Personalised recommendations