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A method for evaluating functional content in mechatronic systems using optimisation


This paper presents a method to support designers and product planners in determining the functionalities that should be implemented in a product and those that should not. The proposed method identifies the set of customer functions and technical implementations that maximise the potential product profit. The customer functions represent the functionality of the product, and the technical implementations are the hardware and software components needed to realise these functions. For industrial applications, the numbers of possible combinations of customer functions and technical implementations are extremely large. We present a mathematical framework that handles this problem. Furthermore, optimisation is employed to find the set of customer functions that will maximise profit when subjected to a restricted development budget in order to find the best possible business case. The method was evaluated on an industrial case study of active safety systems performed at Volvo Cars. Based on this case study, the proposed method shows a substantial potential compared to the methods presently used.

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  1. Andersson J (2001) Multiobjective optimization in engineering design. Dissertation, Dept of Mechanical Engineering, Linköping University, Sweden

  2. Andersson J, Wallace D (2002) Pareto optimization using the struggle genetic crowding algorithm. Eng Optimiz 34:623–644

  3. Birkhofer H (2000) From mechanics to mechatronics—towards a demand-oriented education in machine elements. Institute of Machine Elements and Engineering Design, Dramstadt University of Technology

  4. Blessing L (1994) A process-based approach to computer supported engineering design. Dissertation, University of Twente, The Netherlands

  5. Box MJ (1965) A new method of constraint optimization and a comparison with other methods. Computer J 8:42–52

  6. Bosch (1995) Automotive brake systems. ISBN 1-56091-708-3, Imprimé en Allemage, Stuttgart, pp 38–64

  7. Bylund N, Grante C (2002) System architecture effects on design complexity. In: NordDesign 2002, Trondheim, Norway

  8. Clausing D (1994) Total quality development. ASME Press, New York

  9. Coulouris G, Dollimore J, Kindberg T (2001) Distributed systems, concepts and design. Addison-Wesley, Harlow, pp 17–18

  10. Cross N (2000) Engineering design methods. Wiley, New York

  11. Deb K (1999) Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evolut Comput 7:205–230

  12. Fonseca C, Fleming P (1998) Multiobjective optimization and multiple constraint handling with evolutionary algorithms. 1. A unified formulation. IEEE Trans Syst Man Cybernetics A: Syst Humans 28:26–37

  13. Grante C, Williander M, Krus P, Palmberg JO (2001a) An approach for structuring of design specification for complex systems by optimization. In: Proc 13th international conference on engineering design, Glasgow, Scotland

  14. Grante C, Williander M, Krus P, Palmberg J (2001b) Optimization of design specification for mechatronic automobile systems. In: Proc 7th Scandinavian international conference on fluid power, Linköping, Sweden

  15. Hubka V, Andreasen MM, Eder WE (1988) Practical studies in systematic design. Butterworth, London, p 10, sect 3.2

  16. Isermann R (2000) Diagnosis methods for electronic controlled vehicles. In: AVEC’2000, Ann Arbor, MI, USA

  17. Jurgen R (1999) Automotive electronics handbook. ISBN 0-07-034453-1, McGraw Hill, London, pp 15.1–17.33

  18. Krus P, Janson A, Palmberg JO (1991) Optimization for component selection in hydraulic systems. In: Burrows CR, Edge KA (eds) Fourth Bath international fluid power workshop, University of Bath, Research Studies Press, Baldock, UK

  19. Pahl G, Beitz W (1996) Engineering design. Springer, London

  20. Pareto V (1896) Cours d’économie politique. Rouge, Lausanne

  21. Pugh S (1990) Total design. Addison-Wesley, Workingham

  22. Råde L, Westergren B (1989) Mathematics handbook for science and engineering BETA. Studentlitterature, Lund, p 46

  23. Saaty T (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15:234–281

  24. Schachinger P, Johannesson H (1999) A method for computer based specification of products. Department of Machine and Vehicle Design, Chalmers University, Göteborg, Sweden

  25. Suh N (2001) Axiomatic design. Oxford University Press, New York, pp 10, 16

  26. Thomson G (1999) Improving maintainability and reliability through design. Professional Engineering Publishing, London

  27. Tritle G, Scriven E, Fusfeld A (2000) Resolving uncertainty in R&D portfolios. Res-Technol Manage 43:47–55

  28. Ulrich K, Eppinger D (2000) Product design and development, 2nd edn. McGraw-Hill, Boston

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The authors gratefully acknowledge financial support from Volvo Car Corporation and the Swedish Foundation for Strategic Research through the ENDREA program. Thanks are also due to the anonymous reviewers for their help in improving the quality and readability of this paper.

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Correspondence to Johan Andersson.

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Grante, C., Andersson, J. A method for evaluating functional content in mechatronic systems using optimisation. Res Eng Design 14, 224–235 (2003). https://doi.org/10.1007/s00163-003-0040-6

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  • Product planning
  • Complexity management
  • Design specification
  • Optimisation
  • Genetic algorithms