Abstract
This paper discusses the validation of design methods. The challenges and opportunities in validation are illustrated by drawing an analogy to medical research and development. Specific validation practices such as clinical studies and use of models of human disease are discussed, including specific ways to adapt them to engineering design. The implications are explored for three active areas of design research: robust design, axiomatic design, and design decision making. It is argued that medical research and development has highly-developed, well-documented validation methods and that many specific practices such as natural experiments and model-based evaluations can profitably be adapted for use in engineering design research.
This is a preview of subscription content, access via your institution.
References
American Institute of Aeronautics and Astronautics (1998) Guide for the verification and validation of computational fluid dynamics simulations, AIAA G-077–1998
Argyris C (1991) Teaching smart people how to learn. Harvard Business Review, Reprint No. 91301
Audi R (ed) (1995) The Cambridge dictionary of philosophy. Cambridge University Press, Cambridge
Bland M (1987) An introduction to medical statistics. Oxford University Press, New York
Borror CM, Montgomery DC (2000) Mixed resolution designs as alternatives to Taguchi inner/outer array designs for robust design problems. Qual Reliability Int 16:117–127
Box GEP, Hunter WG, Hunter JS (1978) Statistics for experimenters: an introduction to design, data analysis, and model building. Wiley, New York
Box GEP, Liu PTY (1999) Statistics as a catalyst to learning by scientific method. J Qual Technol 31(1):1–29
Cacciabue PC, Hollnagel E (1995) Simulation of cognitive applications. Expertise and technology: cognition and human computer cooperation. Lawrence Erlbaum Associates, Mahwah, pp 55–73
Chakrabarti A, Morgenstern S, Knaab H (2004) Identification and application of requirements and their impact on the design process: a protocol study. Res Eng Des 15(1):22–39
Doll R, Hill AB (1950) Smoking and carcinoma of the lung: preliminary report. Br Med J 2:739–748
Dorst K, Vermass PE (2005) John Gero’s function–behavior–structure model of designing: a critical analysis. Res Eng Des 16(1):17–26
Dym CL (1994a) Representing designed objects: the languages of engineering design. Arch Comput Methods Eng 1(1):75–108
Dym CL (1994b) Engineering Design: A Synthesis of Views. Cambridge University, New York
Dym CL, Wood WH, Scott MJ (2002) Rank ordering engineering designs: pairwise comparison charts and boorda counts. Res Eng Des 13:236–242
Dym CL (2004) Principles of mathematical modeling, 2nd edn. Elsevier, Boston
Dym CL, Little P (2004) Engineering design: a project-based introduction, 2nd edn. Wiley, New York
Easterbrook PJ, Berlin JA, Gopalan R, Mathews DR (1991) Publication bias in clinical research. Lancet 337:867–872
Fisher RA (1958) Cigarettes, cancer, and statistics. Centennial Rev II(2):151–166
Frey DD, Li X (2004) Validating Robust parameter design methods, DETC2004-57518. In: ASME design engineering technical conference, September 28 to October 2, Salt Lake City, Utah
Gainetdinov RR, Wetsel WC, Jones SR, Levin ED, Jaber M, Caron MG (1999) Role of setotonin in the paradoxical calming effect of psychostimulants on hyperactivity. Science 283:397–401
Griffin A (1989) Evaluating development processes: QFD as an example. Marketing Science Institute Report, pp 91–121
Hasselman TK (2001) Quantification of uncertainty in structural dynamics models. J Aerospace Eng 14(4):158–165
Hasselman TK, Anderson MC, Gan W (1998) Principal components analysis for non-linear model correlation, updating, and uncertainty evaluation. In: Proceedings of the 16th IMAC, Bethel, pp 644–651
Hauser J, Clausing DP (1988) The house of quality, Harvard Business Review
Hazelrigg GA( 1998) A framework for decision-based engineering design. ASME J Mech Des 120:653–658
Hazelrigg GA (1999) An axiomatic framework for engineering design. ASME J Mech Des 121:342–347
Hazelrigg GA (2003) Thoughts on model validation for engineering design, DETC 2003/DTM48632. In: ASME design engineering technical conference, September 2–6, Chicago
Hill BA (1966) The environment and disease: association or causation?. Proc Roy Soc Med 58:295
Hirschi NW, Frey DD (2002) Cognition and complexity: an experiment on the effect of coupling in parameter design. Res Eng Des 13(3):123–131
Institute of Electrical and Electronics Engineers (1998) IEEE Standard for Software Verification and Validation, IEEE Std 1012–1998
Klein G, Ross KG, Moon BM, Klein DE, Hoffman RR, Hollnagel E (2003) Macrocognition. IEEE Intell Syst 18(3):81–84
Kunert J (2004) A comparison of Taguchi’s product array and the combined array in robust-parameter-design. In: Eleventh annual spring research conference (SRC) on statistics in industry and technology, Gaithersburg, May 19–21
Kuppuraju N, Ittimakin P, Mistree F (1985) Design through selection: a method that works. Des Stud 6(2):91–106
Li X, Frey DD (2005) A study of factor effects in design of experiments. DETC2005-85486. In: Proceedings of ASME design engineering technical conferences, Long Beach, September 24, pp 1–10
McAdams DA, Dym CL (2004) Modeling and information in the design process. In: Proceedings of the 2004 ASME design engineering technical conferences, Salt Lake City, September 2004
Moss J, Cagan J (2004) Learning from design experience in an agent-based design system. Res Eng Des 15(2):77–92
Olewnik AT, Lewis KE (2005) On validating engineering design decision support tools. Concurr Eng Res Appl 13(2):111–122
Pedersen K, Emblemsvag J, Bailey R, Allen JK, Mistree F (2000) Validating design methods & research: the validation square. DETC2000/DTM14579. In: Proceedings of the ASME design engineering technical conference, Baltimore
Phadke MS (1989) Quality engineering using robust design. PTR Prentice-Hall, Inc., A Simon & Schuster Company, Englewood Cliffs
Reich Y (1994) Layered models of research methodologies. Artif Intell Eng Des Anal Manuf 8(4):263–274
Reich Y, Kohlberg E, Levin I (2006) Designing contexts for learning design. Int J Eng Educ (in press)
Saari DG (2001) Decisions and elections: explaining the unexpected. Cambridge University Press, New York
Savage LJ (1954) The foundations of statistics. Dover Publications Inc., New York
Schön DA (1983) The reflective practitioner: how professionals think in action. Basic Books, New York
Schön DA, Argyris C (1975) Theory in practice: increasing professional effectiveness. Jossey-Bass, San Fransisco
Simon HA (1990) Invariants of human behavior. Annu Rev Psychol 41:1–19
Simon HA (1996) The sciences of the artificial, 3rd edn. MIT Press, Cambridge
Suh NP (1990) The principles of design. Oxford University Press, Oxford
Suh NP (1998) Axiomatic design theory for systems. Res Eng Des 10:189–209
Taguchi G (1987) System of experimental design: engineering methods to optimize quality and minimize costs. Translated by Tung LW, QualityResources: a Division of the Kraus Organization Limited, White Plains, and American Supplier Institute, Inc. Dearborn, vol 1, pp 1–531
Todd PM, Gigerenzer G (2003) Bounding rationality to the World. J Econ Psychol 24:143–165
U. S. Department of Health and Human Services—Food and Drug Administration (1996) Good clinical practice: consolidated guidance. http://www.fda.gov/cder/guidance/idex.htm
U. S. Department of Health and Human Services—Food and Drug Administration (1998a) Providing clinical evidence of effectiveness for human drug and biological products, http://www.fda.gov/cder/guidance/idex.htm
U. S. Department of Health and Human Services—Food and Drug Administration (1998b) Statistical principles for clinical trials, http://www.fda.gov/cder/guidance/idex.htm
Wilkening S, Sobek DK (2004) Relating design activity to quality of outcome: a regression analysis of student projects. In: ASME international design engineering technical conferences. Salt Lake City, Sept 28–Oct 2
Wu CFJ, Hamada M (2000) Experiments: planning, analysis, and parameter design optimization. Wiley, New York
Acknowledgments
D. D. Frey gratefully acknowledges the financial support of the National Science Foundation (award #0448972) and the Ford/MIT Alliance. The extensive comments offered by Yoram Reich have been very beneficial to the authors in completing this manuscript, as have the suggestions of the anonymous reviewers.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Frey, D.D., Dym, C.L. Validation of design methods: lessons from medicine. Res Eng Design 17, 45–57 (2006). https://doi.org/10.1007/s00163-006-0016-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00163-006-0016-4