Optimization in a flexible die-casting engine-head plant via discrete event simulation

  • E. S. Andrade-Gutierrez
  • S. Y. Carranza-Bernal
  • J. Hernandez-Sandoval
  • A. J. Gonzalez-Villarreal
  • T. P. Berber-Solano


The current research studies a flexible die-casting plant in order to increase productivity pondering investment risks in case of placing new components in the production line. Digital models were developed by means of a Plant Simulation software package. Modeling tools are helpful to represent the movements and functions of the production line components and also to identify the bottlenecks in the production line which improves the decision-making process to increase the productive efficiency. Several numerical models were evaluated; findings suggest significant reductions in the production cycle times which span from 1.13 to 65.25% at the best scenario. The most drastic change in the simulations was to add a new robot to the system improving the process flow. Moreover, the results suggested that the productivity increased for more than 300%, mainly due to the synchronization of the flexible plant elements.


Virtual-manufacturing Plant-simulation-software Casting Foundry Manufacturing Discrete-events Model Simulation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Khan WA, Raouf A, Cheng K (2011) Virtual manufacturing, 1st edn. Springer, London, pp 1–8. Google Scholar
  2. 2.
    Burger N, Demartini M, Tonelli F, Bodendorf F, Testa C (2017) Investigating flexibility as a performance dimension of a Manufacturing Value Modeling Methodology (MVMM): a framework for identifying flexibility types in manufacturing systems. Procedia CIRP 63:33–38. CrossRefGoogle Scholar
  3. 3.
    Silva AL, Ribeiro R, Teixeira M (2017) Modeling and control of flexible context-dependent manufacturing systems. Inf Sci 421:1–14. MathSciNetCrossRefGoogle Scholar
  4. 4.
    Dengzhe DM, Gausemeier J, Fan X (2011) Virtual reality & augmented reality in industry, 2nd edn. Springer, London, pp 22–28Google Scholar
  5. 5.
    Shumaker R, Lackey S (2014) Virtual, augmented and mixed reality: applications of virtual and augmented reality, 6th international conference proceedings Part II, Springer, pp 10–28.
  6. 6.
    Turner CJ, Hutabarat W, Oyekan J, Tiwari A (2016) Discrete event simulation and virtual reality use in industry: new opportunities and future trends. IEEE Trans Human-Machine Syst 46:882–894CrossRefGoogle Scholar
  7. 7.
    Shibin KT, Gunasekaran A, Dubey R (2017) Flexible sustainable manufacturing via decision support simulation: a case study approach. Sustain Prod Consum 12:206–220. CrossRefGoogle Scholar
  8. 8.
    Popovics G, Monostori L (2016) An approach to determine simulation model complexity. Procedia CIRP 52:257–261. CrossRefGoogle Scholar
  9. 9.
    Bangsow S (2016) Tecnomatrix plant simulation. Springer, Berlin and London, pp 108–122. CrossRefGoogle Scholar
  10. 10.
    Zhang M and Matta A (2016) Discrete event optimization: workstation and buffer allocation problem in manufacturing flow lines. Proceeding 2016 Winter Simul Conf, pp 2879–2890Google Scholar
  11. 11.
    Molenda P, Drews T, Oechsle O, Butzer S, Schoetz S, Steinhilper R (2017) The 50th CIRP conference on manufacturing systems: a simulation-based framework for the economic evaluation of flexible manufacturing systems. Procedia CIRP 63:201–206. CrossRefGoogle Scholar
  12. 12.
    Li X and Song Y (2015) Optimization of facilities layout based on lean manufacturing. Proceeding of the 22nd International Conference on Industrial Engineering and Engineering Management, Springer, pp 853–860Google Scholar
  13. 13.
    Chen J, Augenbroe G, Wang Q, Song X (2017) Uncertainty analysis of thermal comfort in a prototypical naturally ventilated office building and its implications compared to deterministic simulation. Energy Build 146:283–294. CrossRefGoogle Scholar
  14. 14.
    Pellegrini R, Serani A, Leotardi C, Iemma U, Campana EF, Diez M (2017) Formulation and parameter selection of multi-objective deterministic particle swarm for simulation-based optimization. Appl Soft Comput J 58:714–731CrossRefGoogle Scholar
  15. 15.
    Guan Z, Cao L, Wang C, Cui Y, Shao X (2011) Simulation of logistics system with aspect of pallet requirements optimization based on digital factory. Adv Autom Robot 1:293–302Google Scholar
  16. 16.
    Trebuňa P, Kliment M, Edl M, Petrik M (2014) Creation of simulation model of expansion of production in manufacturing companies. Procedia Eng 96:477–482. CrossRefGoogle Scholar
  17. 17.
    Kliment M, Popovič R, Janek J (2014) Analysis of the production process in the selected company and proposal a possible model optimization through PLM software module tecnomatix plant simulation. Procedia Eng 96:221–226. CrossRefGoogle Scholar
  18. 18.
    Kruse A, Butzer S, Drews T, Steinhilper R (2015) A Simulation-based framework for improving the ecological and economic transparency in multi-variant production. Procedia CIRP 26:179–184. CrossRefGoogle Scholar
  19. 19.
    Bako B, Božek P (2016) Trends in simulation and planning of manufacturing companies. Procedia Eng 149:571–575. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • E. S. Andrade-Gutierrez
    • 1
  • S. Y. Carranza-Bernal
    • 1
  • J. Hernandez-Sandoval
    • 1
  • A. J. Gonzalez-Villarreal
    • 2
  • T. P. Berber-Solano
    • 1
  1. 1.Facultad de Ingeniería Mecánica y Eléctrica, Universidad Autónoma de Nuevo LeónCiudad UniversitariaSan Nicolás de los GarzaMexico
  2. 2.Corporativo Nemak S.A. de C.VGarza GarcíaMexico

Personalised recommendations