Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-25T02:51:37.708Z Has data issue: false hasContentIssue false
  • English
  • Français

Information generation during design: Information importance and design effort

Published online by Cambridge University Press:  22 July 2005

A.J. DENTSORAS
A.J. DENTSORAS
Affiliation:
Machine Design Laboratory, Department of Mechanical Engineering and Aeronautics, University of Patras, 26500 Patras, Greece
Get access Rights & Permissions [Opens in a new window]

Abstract

The present paper studies the process of information generation during design and focuses on the relationship between the information importance and the required effort for its generation. Multiple associative relationships among design entities (handled as design descriptors) are used to represent the design knowledge. The characteristics of the dependent and the primary descriptors are examined and their distinct roles in the design process are discussed. Term definitions concerning the information importance and the design effort are also introduced. The descriptors are used to form a matrix. A number of operations on this matrix results in its transformation, with the final matrix reflecting the quantitative relationship between the information importance and the design effort. From the aforementioned matrix, a unique sorted list for the primary design descriptors is produced. Following this list during descriptor instantiation ensures the production of design information of maximum importance with the least effort in the early design stages. The design of a belt conveyor is used as a basis for a better understanding of the theoretical analysis and for a demonstration of the use of the suggested descriptor list.

Keywords

DesignDesign DescriptorsDesign EffortInformation Importance
Type
Research Article
Information
AI EDAM , Volume 19 , Issue 1 , February 2005 , pp. 19 - 32
Copyright
2005 Cambridge University Press

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

Alles, R. (1988). Conveyor Belt System Design. Hanover, Germany: Continental Aktiengesellschaft Publishers.
Austin, S., Baldwin, A., & Newton, A. (1994). Manipulating the flow of design information to improve the programming of building design. Construction Management & Economics 12(5), 445–455.CrossRefGoogle Scholar
Black, T.A., Fine, C.H., & Sachs, E.M. (1990). A Method for Systems Design Using Precedence Relationships: An Application to Automotive Brake Systems. Working Paper No. 3208. Cambridge, MA: MIT Sloan School of Management.
Browning, T.R. (2001). Applying the design structure matrix to system decomposition and integration problems: a review and new directions. IEEE Transactions on Engineering Management 48(3), 292–306.CrossRefGoogle Scholar
Dentsoras, A.J. (1996). An Approach of routine design based on extensive design space search. Proc. IITT Int. Conf. EXPERSYS-96.
Drakatou, S. & Dentsoras, A.J. (2001). A method for the automatic deduction of priority lists of entities and tasks from the design knowledge. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 15(3), 223–232.CrossRefGoogle Scholar
Duffey, M.R. & Dixon, J.R. (1990). A program of research in mechanical design: computer-based models and representations. Mechanical Machine Theory 25(3), 383–395.CrossRefGoogle Scholar
Eppinger, S.D. (1991). Model-based approaches to managing concurrent engineering. Journal of Engineering Design 2, 283–290.CrossRefGoogle Scholar
Franceschini, F. & Rossetto, S. (2002). QFD: an interactive algorithm for the prioritization of product's technical design characteristics. Integrated Manufacturing Systems 13(1), 69–75.CrossRefGoogle Scholar
Kusiak, A. & Larson, N. (1995). Decomposition and representation methods in mechanical design. ASME Transactions: Journal of Mechanical Design 117(3), 17–24.CrossRefGoogle Scholar
Kusiak, A. & Park, K. (1990). Concurrent engineering: decomposition and scheduling of design activities. International Journal of Production Research 28(10), 1883–1900CrossRefGoogle Scholar
Kusiak, A. & Wang, J. (1993). Decomposition of the design process. Journal of Mechanical Design 115, 687–695.CrossRefGoogle Scholar
Lee, C.-H., Sause, & R., Hong N.K. (1998). Overview of entity-based integrated design product and process models. Advances in Engineering Software 29(10), 809–823.CrossRefGoogle Scholar
Lu, S.C.-Y. & Tcheng, D.K. (1991). Building layered models to support engineering decision making: a machine learning approach. Journal of Engineering for Industry 113(1), 1–9.CrossRefGoogle Scholar
MacCallum, K.J. & Duffy, A. (1987). An expert system for preliminary numerical design modeling. Design Studies 8(4), 231–237.CrossRefGoogle Scholar
Shaalan, K., Rafea, M., & Rafea, A. (1998). KROL, a knowledge representation object language on top of Prolog. Expert Systems With Applications 15(1), 33–46.CrossRefGoogle Scholar
Shooter, S.B., Keirouz, W.T., Szykman, S., & Fenves, S.J. (2000). A model for the flow of design information in product development. Engineering with Computers 16, 178–194.CrossRefGoogle Scholar
Sim, S.K. & Duffy, A.H.B. (1998). A foundation for machine learning in design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12, 193–209.CrossRefGoogle Scholar
Smith, R.P. & Eppinger, S.D. (1997). Identifying controlling features of engineering design iteration. Management Science 43(3), 276–293.CrossRefGoogle Scholar
Spivakovsky, A. & Dyachkov, V. (1985). Conveying Machines. Moscow: Mir Publ.
Steward, D.V. (1981). The design structure system: a method for managing the design of complex systems. IEEE Transactions on Engineering Management 28, 71–74.CrossRefGoogle Scholar
Suh, N.P. (1990). The Principles of Design. Oxford Series in Advanced Manufacturing. New York: Oxford University Press.
Tsalidis, S.S. & Dentsoras, A.J. (1997). Application of design parameters space search for belt conveyor design. Engineering Application of Artificial Intelligence 10(6), 617–629.CrossRefGoogle Scholar
Warfield, J.N. (1973). Binary matrices in system modeling. IEEE Transactions on Systems, Man, and Cybernetics 3, 441–449.CrossRefGoogle Scholar
Yagiu, T. (1989). A predicate—logical method for modeling design objects. Artificial Intelligence in Engineering 4(1), 41–53.CrossRefGoogle Scholar
Yassine, A., Falkenburg, D., & Chelst, K. (1999). Engineering design management: an information structure approach. International Journal of Production Research 37(13), 2957–2975.CrossRefGoogle Scholar