Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-28T17:44:35.740Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluation framework for the design of an engineering model

Published online by Cambridge University Press:  21 May 2009

Walid Ben Ahmed ,
Mounib Mekhilef ,
Bernard Yannou  and
Michel Bigand
Walid Ben Ahmed
Affiliation:
Laboratoire Genie Industriel, Ecole Centrale Paris, Châtenay-Malabry, France
Mounib Mekhilef
Affiliation:
IUFM, Department of Mathematics, University of Orleans, Bourges, France
Bernard Yannou
Affiliation:
Laboratoire Genie Industriel, Ecole Centrale Paris, Châtenay-Malabry, France
Michel Bigand
Affiliation:
Ecole Centrale de Lille, Laboratoire de Génie Industriel, Lille, France
Get access Rights & Permissions [Opens in a new window]

Abstract

According to both cybernetics and general system theory, a subject develops and uses an adequate model of a system to widen his/her knowledge about the system. Models are then the interface between a subject and a real-world system to solve a problem and to construct knowledge. Hence, evaluating these models is crucial to ensure the quality of the constructed knowledge. We propose here an evaluation framework to assess complex models based on the intrinsic properties of these models as well as the properties of the derived knowledge. A series of 40 evaluation criteria are proposed under the four systemic axes: ontology, functioning, evolution, and teleology. Through a case study, we show how our evaluation model allows both presenting a given model and assessing it.

Keywords

CyberneticsEvaluation CriteriaKnowledge EvaluationModel Evaluation
Type
Regular Articles
Information
AI EDAM , Volume 24 , Issue 1: Design Computing and Cognition , February 2010 , pp. 107 - 125
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Ashby, W.R. (1956). An Introduction to Cybernetics (Hall, C., Ed.). London: Chapman & Hall.CrossRefGoogle Scholar
Baud, N., Mekhilef, M., & Bocquet, J.-C. (1999). Proposal of a functional model of logistics for spare parts preservation. Proc. Integrated Design and Manufacturing in Mechanical Engineering Conf., IDMME, pp. 561–568.CrossRefGoogle Scholar
Ben Ahmed, W., Mekhilef, M., Bigand, M., & Page, Y. (2003). Intégration des connaissances du domaine pour la fouille de données. EGC 2004, Journées Francophones d'Extraction et de Gestion de Connaissances, Université Blaise Pascal, Clermont Ferrand, France, January 20–24.Google Scholar
Ben Ahmed, W., & Yannou, B. (2009). A Bayesian learning of probabilistic relations between perceptual attributes and technical characteristics of car dashboards to construct a perceptual evaluation model. International Journal of Product Development 7, 47–72.CrossRefGoogle Scholar
Bertalanffy, L.V. (1969). General System Theory: Foundations, Development, Applications. New York: George Braziller.Google Scholar
Campbell, D.T. (1974). Evolutionary epistemology. In The Philosophy of Karl Popper (Schilpp, P.A., Ed.), pp. 413–463. La Salle, IL: Open Court Publishers.Google Scholar
Cantzler, O., Mekhilef, M., & Bocquet, J.-C. (1995). A systemic approach to corporate knowledge: an ontology to process modelling in a design department. IEEE Transactions on Systems, Man, and Cybernetics 1(1), 153–158.Google Scholar
Harvey, A. (2005). Application of an integrated method to a study of the consumer perceptions of automobile dashboards. Research Internship Report, Ecole Centrale Paris, University of Bath.Google Scholar
Henry, G.T. (2003). Influential evaluation. American Journal of Evaluation 24(4), 515–524.CrossRefGoogle Scholar
Henry, G.T., & Mark, M.M. (2003). Beyond use: understanding assessment's influence on attitudes and actions. American Journal of Evaluation 24 ( 3), 293–314.Google Scholar
Heylighen, F. (1993). Selection criteria for the evolution of knowledge. Proc. 13th Int. Congress on Cybernetics, Association Internationale de Cybernétique, Namur.Google Scholar
Heylighen, F. (1997). Objective, subjective and intersubjective selectors of knowledge. Evolution and Cognition 3(1), 63–67.Google Scholar
Huang, C.A., & Dawiche, A. (1996). Inference in belief networks: a procedural guide. International Journal of Approximate Reasoning 15, 225–263.CrossRefGoogle Scholar
Jensen, F.V. (1996). An Introduction to Bayesian Networks. London: UCL Press.Google Scholar
Kirkhart, K. (2000). Reconceptualizing evaluation use: an integrated theory of influence. In The Expanding Scope of Evaluation Use. New Directions for Evaluation (Caracelli, V., & Preskill, H., Eds.), pp. 5–24. San Fransisco, CA: Jossey–Bass.Google Scholar
Lam, W., & Bacchus, F. (1994). Learning Bayesian belief networks: an approach based on the MDL principle. Computational Intelligence 10, 269–293.CrossRefGoogle Scholar
Le Moigne, J.-L. (1999). La Modélisation des Systèmes Complexes. Paris: Dunod.Google Scholar
Limayem, F., & Yannou, B. (2004). Generalization of the RCGM and LSLR pairwise comparison methods. Computers and Mathematics With Applications 48, 539–548.CrossRefGoogle Scholar
Mekhilef, M., Bocquet, J.-C., Cantzler, O., & Gallardo, J.-F. (1998). Towards a conceptual architecture for the capitalization of design process. Proc. Integrated Design and Manufacturing for Mechanical Engineering Conf., IDMME, pp. 1037–1044.Google Scholar
Nagamachi, M. (1997). Kansei Engineering: The Framework and Methods. Kansei Engineering 1 (Nagamachi, M., Ed.), pp. 1–9. Kure, Japan: Kaibundo Publishing.Google Scholar
Najm, W.G., Smith, J.D., & Smith, D.L. (2001). Analysis of Crossing Path Crashes. Report No. DOT HS 809 423. Washington, DC: National Highway Traffic Safety Administration.Google Scholar
Petiot, J.-F., & Yannou, B. (2004). Measuring consumer perceptions for a better comprehension, specification and assessment of product semantics. International Journal of Industrial Ergonomics 33(6), 507–525.CrossRefGoogle Scholar
Reich, Y. (1994). Layered models of research methodologies. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 8(4), 263–274,CrossRefGoogle Scholar
Reich, Y. (1995). Measuring the value of knowledge. International Journal of Human–Computer Studies 42(1), 3–30.CrossRefGoogle Scholar
Schütte, S. (2005). Engineering emotional values in product design: kansei engineering in development. Dissertation 951, Linköping University, Institute of Technology.Google Scholar
Sowa, J.F. (1984). Conceptual Structures, Information Processing in Mind and Machine. Boston: Addison–Wesley Longman.Google Scholar
Suh, N. (1993). The Principles of Design. Oxford: Oxford University Press.Google Scholar
Turchin, V. (1991). Cybernetics and philosophy. In The Cybernetics of Complex Systems—Self-Organization, Evolution and Social Change (Geyer, F., Ed.), Salinas, CA: Inter-Systems.Google Scholar
Von Foerster, H. (1995). The Cybernetics of Cybernetics, 2nd ed.Minneapolis, MN: Future Systems.Google Scholar
Yannou, B., & Coatanea, E. (2007). Easy and flexible specifications and product evaluations by expert and customer comparisons with existing products. Int. Conf. Engineering Design, ICED ’07, Cité des Sciences et de l'Industrie, Paris, August 28–31.Google Scholar
Yannou, B., & Petiot, J.-F. (2004). A methodology for integrating the customers’ assessments during the conceptual design. Proc. ASME Design Engineering Technical Conf., Design Theories and Methodologies, Salt Lake City, UT, September 28–October 2.CrossRefGoogle Scholar