A performance-based design framework for enhancing decision-making at the conceptual phase of a motorcycle rear suspension development

Abstract

The functional design of a motorcycle rear suspension has become a complex process which involves different engineering disciplines such as computer aided design, structural analysis or multibody simulations. As a consequence of this multidiciplinarity, its development process is surrounded by multiple inter-related aspects and uncertainties which can compromise the feasibility of the solutions and hence making it difficult to foresees a priori the most appropriated design directions. This paper proposes an integrated methodology that supports early stage design decision-making for motorcycle rear suspensions by providing a rapid generative mechanism of feasible solutions with performance feedback for multiple requirements. The proposed framework integrates an object-oriented representation of the rear suspension with an adaptative design space approach for enhancing the capability to generate a variety of feasible solutions. A generative system coupled with the NSGA-II algorithm is proposed as responsible for exploring and managing the optimal functional design. The workflow has been structured in such a way all the design actions are conducted automatically. A case study of a Premoto3 rear suspension design is included in order to illustrated the effectiveness of the presented framework.

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References

  1. Abraham A, Jain L (2005) Evolutionary multiobjective optimization. In: Abrahan A, Jain L, Goldberg R (eds) Evolutionary multiobjective optimization. Theoretical advances and applications. Springer, London, pp 1–6

    Google Scholar 

  2. Barbagallo R, Sequenzia G, Cammarata A, Oliveri SM, Fatuzzo G (2018) Redesign and multibody simulation of a motorcycle rear suspension with eccentric mechanism. Int J Interact Des Manuf (IJIDeM) 12:517–524

    Article  Google Scholar 

  3. Bendsoe MP, Sigmund O (1999) Material interpolation schemes in topology optimization. Arch Appl Mech 69:635–654

    Article  Google Scholar 

  4. Brailsford SC, Potts CN, Smith BM (1999) Constraint satisfaction problems: algorithms and applications. Eur J Oper Res 119:557–581

    Article  Google Scholar 

  5. Budynas RG, Nisbett JK (2008) Shigley’s mechanical engineering design, vol 8. McGraw-Hill, New York

    Google Scholar 

  6. Caraballo SC, Rodrguez JO, Ruiz JL, Fernndez R (2017) Optimization of a butterfly valve disc using 3D topology and genetic algorithms. Struct Multidiscip Optim 56:941–957

    Article  Google Scholar 

  7. Caraballo SC, Fernndez R, Ruiz JL (2018) Integration of cutting time into the structural optimization process: application to a spreader bar design. Struct Multidiscip Optim 58:2269–2289

    Article  Google Scholar 

  8. Castillo JJ, Giner P, Simn A, Cabrera JA (2013) Optimal design of motorcycle rear suspension systems using genetic algorithms. In: Viadero F, Ceccarelli M (eds) New trends in mechanism and machine science. Springer, Dordrecht, pp 181–189

    Google Scholar 

  9. Christiansen AN, Brentzen JA, Nobel-Jrgensen M, Aage N, Sigmund O (2015) Combined shape and topology optimization of 3D structures. Comput Graph 46:25–35

    Article  Google Scholar 

  10. Coello CAC, Lamont GB (2004) Applications of multi-objective evolutionary algorithms, vol 1. World Scientific, London

    Book  Google Scholar 

  11. Corbera S, Olazagoitia JL, Lozano JA (2016) Multi-objective global optimization of a butterfly valve using genetic algorithms. ISA Trans 63:401–412

    Article  Google Scholar 

  12. Cossalter V (2006) Motorcycle dynamics. Lulu.com

  13. Cossalter V, Lot R (2002) A motorcycle multi-body model for real time simulations based on the natural coordinates approach. Veh Syst Dyn 37:423–447

    Article  Google Scholar 

  14. Cossalter V, Doria A, Lot R (2000) Optimum suspension design for motorcycle braking. Veh Syst Dyn 34:175–198

    Article  Google Scholar 

  15. De Jalon JG, Bayo E (2012) Kinematic and dynamic simulation of multibody systems: the real-time challenge. Springer, Berlin

    Google Scholar 

  16. Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6:182–197

    Article  Google Scholar 

  17. Eckert C, Kelly I, Stacey M (1999) Interactive generative systems for conceptual design: an empirical perspective. AI EDAM 13:303–320

    Google Scholar 

  18. El Amine M, Pailhes J, Perry N (2017) Integration of concept maturity in decision-making for engineering design: an application to a solar collector development. Res Eng Des 28:235–250

    Article  Google Scholar 

  19. Fossati GG, Miguel LFF, Casas WJP (2019) Multi-objective optimization of the suspension system parameters of a full vehicle model. Optim Eng 20:151–177

    Article  Google Scholar 

  20. Giner DM (2016) Symbolic-numeric tools for the analysis of motorcycle dynamics. Development of a virtual rider for motorcycles based on model predictive control. Dissertation, Universidad Miguel Hernndez de Elche

  21. Giner DM, Manka M (2009) Motorcycle dynamic models for virtual rider design and cornering analysis. In: ASME 2009 international design engineering technical conferences and computers and information in engineering conference. American Society of Mechanical Engineers pp 869–878

  22. Hassani B, Hinton E (1998) A review of homogenization and topology optimization IIItopology optimization using optimality criteria. Comput Struct 69:739–756

    Article  Google Scholar 

  23. Hien NT, Hoai NX (2006) A brief overview of population diversity measures in genetic programming. In: Proceedings of 3rd Asian-Pacific workshop on genetic programming. Hanoi, Vietnam pp 128–139

  24. Howard TJ, Dekoninck EA, Culley SJ (2010) The use of creative stimuli at early stages of industrial product innovation. Res Eng Des 21:263–274

    Article  Google Scholar 

  25. Jang IG, Kwak BM (2008) Design space optimization using design space adjustment and refinement. Struct Multidiscip Optim 35:41–54

    MathSciNet  Article  Google Scholar 

  26. Kim IY, Kwak BM (2002) Design space optimization using a numerical design continuation method. Int J Numer Methods Eng 53:1979–2002

    Article  Google Scholar 

  27. Kiusalaas J (2013) Numerical methods in engineering with Python 3. Cambridge University Press, Cambridge

    Book  Google Scholar 

  28. Knigseder C, Shea K (2016) Comparing strategies for topologic and parametric rule application in automated computational design synthesis. J Mech Des 138:011102

    Article  Google Scholar 

  29. Krish S (2011) A practical generative design method. Comput Aided Des 43:88–100

    Article  Google Scholar 

  30. Liu K, Tovar A (2014) An efficient 3D topology optimization code written in Matlab. Struct Multidiscip Optim 50:1175–1196

    MathSciNet  Article  Google Scholar 

  31. Maggio F (2009) Multi-body simulation and multi-objective optimization applied to vehicle dynamics. Int J Simul Multidiscip Des Optim 3:411–416

    Article  Google Scholar 

  32. Morrison RW, De Jong KA (2001) Measurement of population diversity. In: International conference on artificial evolution (Evolution Artificielle). Springer, Berlin, pp 31–41

  33. Patel NM, Tillotson D, Renaud JE, Tovar A, Izui K (2008) Comparative study of topology optimization techniques. AIAA J 46:1963–1975

    Article  Google Scholar 

  34. Pugliese MJ, Cagan J (2002) Capturing a rebel: modeling the Harley-Davidson brand through a motorcycle shape grammar. Res Eng Des 13:139–156

    Article  Google Scholar 

  35. Sharp RS, Evangelou S, Limebeer DJ (2004) Advances in the modelling of motorcycle dynamics. Multibody Syst Dyn 12:251–283

    Article  Google Scholar 

  36. Shea K, Aish R, Gourtovaia M (2005) Towards integrated performance-driven generative design tools. Autom Constr 14:253–264

    Article  Google Scholar 

  37. Sigmund O (2001) A 99 line topology optimization code written in Matlab. Struct Multidiscip Optim 21:120–127

    Article  Google Scholar 

  38. Sigmund O, Maute K (2013) Topology optimization approaches. Struct Multidiscip Optim 48:1031–1055

    MathSciNet  Article  Google Scholar 

  39. Stan C (2008) Development trends of motorcycles: with 10 tables, page numbers. Expert-Verlag, Renningen

    Google Scholar 

  40. Stiny G, Gips J (1971) Shape grammars and the generative specification of painting and sculpture. In: IFIP Congress 2(3)

  41. Tang PS, Chang KH (2001) Integration of topology and shape optimization for design of structural components. Struct Multidiscipl Optim 22:65–82

    Article  Google Scholar 

  42. Uicker JJ, Ravani B, Sheth PN (2013) Matrix methods in the design analysis of mechanisms and multibody systems. Cambridge University Press, Cambridge

    Book  Google Scholar 

  43. Wynn DC, Clarkson PJ (2018) Process models in design and development. Res Eng Des 29:161–202

    Article  Google Scholar 

  44. Yang D, Dong M (2013) Applying constraint satisfaction approach to solve product configuration problems with cardinality-based configuration rules. J Intell Manuf 24:99–111

    Article  Google Scholar 

  45. Yan J, Li C, Wang Z, Deng L, Sun D (2007) Diversity metrics in multi-objective optimization: review and perspective. In: 2007 IEEE international conference on integration technology, IEEE pp 553–557

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Correspondence to Sergio Corbera Caraballo.

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Corbera Caraballo, S., Alvarez Fernandez, R. A performance-based design framework for enhancing decision-making at the conceptual phase of a motorcycle rear suspension development. Optim Eng 21, 1283–1317 (2020). https://doi.org/10.1007/s11081-019-09475-w

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Keywords

  • Functional design
  • Generative grammars
  • Evolutionary algorithms
  • Motorcycle design
  • Computational design synthesis