Advertisement

From Simulation to Contradictions, Different Ways to Formulate Innovation Directions

  • Sébastien DuboisEmail author
  • Hicham Chibane
  • Roland De Guio
  • Ivana Rasovska
Chapter

Abstract

The main purpose of this paper is to show to what extent data used in design optimization process can be used to provide innovation directions and inputs to TRIZ methods. When looking for a new design, it is common to first try to optimize existing systems by experimental and numerical means. This approach requires building a model linking on the one hand, a set of Action Parameters and their range of possible values; and on the other hand, Evaluation Parameters that allow measuring the quality of a solution. Next, to evaluate the best potential solutions, the concept of dominance can be used to define the Pareto frontier, somehow the limits of the performances that can be reached with the built model of system. When none of the dominant points satisfies the objectives, it means that a redesign of the system is required and directions towards this new design needs to be elicited. Our hypothesis in this paper is that some directions can be formulated out of the analysis of experimental or simulation data, either by interpreting the influence of each parameter towards the reaching of the objectives, which is the classical routine way to do, or by identifying systems of contradictions from the data and thus propose another way to overcome the Pareto frontier.

References

  1. 1.
    Burgard L, Dubois S, De Guio R et al (2011) Sequential experimentation to perform the analysis of initial situation. In: Cascini G, Vaneker T (eds) TRIZ Future Conference 2011. Institute of Technology Tallaght, Dublin, pp 35–45Google Scholar
  2. 2.
    Dubois S, Lin L, De Guio R et al (2015) From simulation to invention, beyond the Pareto-frontier. In: Christian Weber SH, CantaMESsa M, Cascini G, Marjanovic D, Graziosi S (eds) 20th international conference on engineering design (ICED). Design Society, Milano, pp 245–254Google Scholar
  3. 3.
    Khomenko N, Guio RD, Cavallucci D (2009) Enhancing ECN’s abilities to address inventive strategies using OTSM-TRIZ. International Journal of Collaborative Engineering 1(1/2):98CrossRefGoogle Scholar
  4. 4.
    Dubois S, Eltzer T, De Guio R (2009) A dialectical based model coherent with inventive and optimization problems. Comput Ind 60(8):575–583CrossRefGoogle Scholar
  5. 5.
    Khomenko N, De Guio R, Lelait L et al (2007) A framework for OTSM-TRIZ-based computer support to be used in complex problem management. Int J Comput Appl Technol 30((1) spécial issue Trends in computer aided innovation):88–104CrossRefGoogle Scholar
  6. 6.
    Rasovska I, Dubois S, De Guio R (2009) Mechanisms of model change in optimization and inventive problem solving methods. In: International conference on engineering design, ICED’09, Stanford, CA, pp 24–27 August 2009Google Scholar
  7. 7.
    Dubois S, De Guio R, Rasovska I (2010) Different ways to identify generalized system of contradictions, a strategic meaning. In: Triz Future Conference 2010, Bergamo, pp 119–125Google Scholar
  8. 8.
    Marler RT, Arora JS (2010) The weighted sum method for multi-objective optimization: new insights. Struct Multidiscip Optim 41(6):853–862CrossRefGoogle Scholar
  9. 9.
    Zadeh LA (1963) Optimality and non-scalar-valued performance criteria. IEEE Trans Autom Control 8:59–60CrossRefGoogle Scholar
  10. 10.
    Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann ArborGoogle Scholar
  11. 11.
    Haupt RL, Haupt SE (2004) Practical genetic algorithms with CD-ROM. Wiley, Hoboken, NJGoogle Scholar
  12. 12.
    Saravanan R, Asokan P, Sachidanandam M (2002) A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations. Int J Mach Tool Manu 42(12):1327–1334CrossRefGoogle Scholar
  13. 13.
    Cus F, Balic J (2003) Optimization of cutting process by GA approach. Robot Comput Integr Manuf 19(1–2):113–121CrossRefGoogle Scholar
  14. 14.
    Marler RT, Arora JS (2004) Survey of multi-objective optimization methods for engineering. Struct Multidiscip Optim 26(6):369–395CrossRefGoogle Scholar
  15. 15.
    Chibane H (2013) Contribution to the multi-objective optimization of cutting parameters in machining and the contribution of vibratory analysis: application to metallic and composite materials. Doctorate thesisGoogle Scholar
  16. 16.
    Serra R, Chibane H (2010) Effects of cutting parameters during turning 100C6 steel. 14th International Conference on Experimental Mechanics, 6, 13004Google Scholar
  17. 17.
    Chibane H, Serra R, Leroy R (2011) Optimization of the cutting parameters in turning 3rd Journées Identification and Experimental Modeling (JIME), Douai, FranceGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sébastien Dubois
    • 1
    Email author
  • Hicham Chibane
    • 1
  • Roland De Guio
    • 1
  • Ivana Rasovska
    • 1
  1. 1.CSIPICube LaboratoryStrasbourgFrance

Personalised recommendations