Skip to main content Accessibility help
×
Home

Introduction to quantitative engineering design methods via controls engineering

  • Briana M. Lucero (a1), Matthew J. Adams (a2) and Cameron J. Turner (a3)

Abstract

Functional modeling is an effective method of depicting products in the design process. Using this approach, product architecture, concept generation, and physical modeling all contribute to the design process to generate a result full of quality and functionality. The functional basis approach provides taxonomy of uniform vocabulary to produce function structures with consistent functions (verbs) and flows (nouns). Material and energy flows dominate function structures in the mechanical engineering domain with only a small percentage including signal flows. Research suggests that the signal flow gap is due to the requirement of “carrier” flows of either material or energy to transport the signals between functions. This research suggests that incorporating controls engineering methodologies may increase the number of signal flows in function structures. We show correlations between the functional modeling and controls engineering in four facets: schematic similarities, performance matching through flows, mathematical function creation using bond graphs, and isomorphic matching of the aforementioned characteristics allows for analogical solutions. Controls systems use block diagrams to represent the sequential steps of the system. These block diagrams parallel the function structures of engineering design. Performance metrics between the two domains can be complimentary when decomposed down to nondimensional engineering units. Mathematical functions of the actions in controls systems can resemble the functional basis functions with bond graphs by identifying characteristic behavior of the functions on the flows. Isomorphic matching, using the schematic diagrams, produces analogies based upon similar functionality and target performance metrics. These four similarities bridge the mechanical and electrical domains via the controls domain. We provide concepts and contextualization for the methodology using domain-agnostic examples. We conclude with suggestion of pathways forward for this preliminary research.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Introduction to quantitative engineering design methods via controls engineering
      Available formats
      ×

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Introduction to quantitative engineering design methods via controls engineering
      Available formats
      ×

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Introduction to quantitative engineering design methods via controls engineering
      Available formats
      ×

Copyright

Corresponding author

Reprint requests to: Briana Lucero, Applied Engineering Technologies, Los Alamos National Laboratory, MS-H821, P.O. Box 1663, Los Alamos, NM 87545, USA. E-mail: blucer@lanl.gov

References

Hide All
Åström, K.J., & Kumar, P.R. (2014). Control: a perspective. Automatica 50(1), 343.
Ball, L.J., Ormerod, T.C., & Morley, N.J. (2004). Spontaneous analogising in engineering design: a comparative analysis of experts and novices. Design Studies 25(5), 495508.
Borutzky, W. (2010). Bond Graph Methodology: Development and Analysis of Multidisciplinary Dynamic System Models. London: Springer.
Bracewell, R.H., Shea, K., Langdon, P., Blessing, L., & Clarkson, P. (2001). A methodology for computational cognitive modelling. Proc. Int. Conf. Engineering Design, pp. 181188. Glasgow: Design Society.
Bracewell, R.H., Bradley, D.A., Chaplin, R.V., Langdon, P., & Sharpe, J.E.E. (1993). Schemebuilder, a design aid for the conceptual stages of product design. Proc. Int. Conf. Engineering Design. Zurich: Design Society.
Bruza, P.D., & van der Weide, T.P. (1989). The semantics of data flow diagrams. Proc. Int. Conf. Management of Data (Prakash, I.N., Ed.), pp. 6678, Hyderabad, India.
Cao, Y., Liu, Y., & Paredis, C.J.J. (2011). System-level model integration of design and simulation for mechatronic systems based on SysML. Mechatronics 21(6), 10631075.
Casakin, H., Goldschmidt, G., & Planning, T. (1999). Expertise and the use of visual analogy: implications for design education. Design Studies 20(2), 153175.
Coatanéa, E. (2005). Conceptual modelling of life cycle design: a modelling and evaluation method based on analogies and dimensionless numbers. PhD Thesis. Laboratory of Machine Design, Helsinki University of Technology.
de Kleer, J., & Brown, J.S. (1984). A qualitative physics based on confluences. Artificial Intelligence 24(1–3), 783.
Deng, Y.M. (2002). Function and behavior representation in conceptual mechanical design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16(5), 343362.
Deng, Y.M., Britton, G.A., & Tor, S.B. (2000). Constraint-based functional design verification for conceptual design. Computer-Aided Design 32, 889899.
Deng, Y.M., Tor, S.B., & Britton, G.A. (1999). A computerized design environment for functional modeling of mechanical products. Proc. 5th ACM Symp. Solid Modeling and Applications, pp. 112, Ann Arbor, MI, June 8–11.
Dorf, R., & Bishop, R. (1998). Modern Control Systems, 8th ed. Boston: Addison-Wesley.
Doyle, J., Francis, B., & Tannenbaum, A. (1990). Feedback Control Theory. New York: Macmillan.
Enderle, J., & Bronzino, J. (2012). Introduction to Biomedical Engineering (Bronzino, J., Ed.), 3rd ed. Waltham, MA: Elsevier.
Fenves, S.J. (2002). A Core Product Model for Representing Design Information. Washington, DC: US Department of Commerce, Technology Administration, National Institute of Standards and Technology.
Friedenthal, S., Moore, A., & Steiner, R. (Eds.) (2012). A Practical Guide to SysML, 2nd ed. Waltham, MA: Elsevier.
Friedenthal, S., Moore, A., & Steiner, R. (2015). Modeling event-based behavior with state machines. In A Practical Guide to SysML (Friedenthal, S., Moore, A., & Steiner, R., Eds.), pp. 273294. Waltham, MA: Elsevier.
Fu, K., Chan, J., Cagan, J., Kotovsky, K., Schunn, C., & Wood, K. (2013). The meaning of “near” and “far”: the impact of structuring design databases and the effect of distance of analogy on design output. Journal of Mechanical Design 135(2), 21.
Fu, K., Murphy, J., Yang, M., Kevin, O., Jensen, D., & Wood, K.L. (2013). Investigating the effect of functionality level of analogical stimulation on design outcomes. Proc. Korea-Japan Design Engineering Workshops, Kitakyushu, Fukuoka, Japan, November 28–30.
Gentner, D. (1983). Structure-mapping: a theoretical framework for analogy. Cognitive Science 7, 15.
Gentner, D., & Gentner, D.R. (1982). Flowing Water or Teeming Crowds: Mental Models of Electricity. Washington, DC: Office of Naval Research Personnel and Traing Research Programs.
Gero, J.S. (1990). Design prototypes: a knowledge representation schema for design. AI Magazine 11(4).
Goel, A.K., & Bhatta, S.R. (2004). Use of design patterns in analogy-based design. Advanced Engineering Informatics 18(2), 8594.
Goel, A.K., Rugaber, S., & Vattam, S. (2009). Structure, behavior and function of complex systems: the SBF modeling language. Language 23(1), 2335.
Goel, A.K., Zhang, G., Wiltgen, B., Zhang, Y., Vattam, S., & Yen, J. (2015). On the benefits of digital libraries of case studies of analogical design: documentation, access, analysis, and learning. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 29(2), 215227.
Hampson, K. (2015). ScienceDirect technical evaluation of the systems modeling language (SysML). Procedia Computer Science 44, 403412.
Hutcheson, R.S., Ryan, S., McAdams, D., Stone, R., & Tumer, I. (2007). Function-based systems engineering (Fuse). Proc. Int. Conf. Engineering Design, pp. 112, Paris, August 28–31.
Jayaram, M.B. (2002). A Formal Method for Functional Modeling and Conceptual Design of Complex Mechantronix Systems. Toronto: University of Toronto Press.
Jayaram, M., Chen, L., & Xi, F. (2003). Functional modeling of complex mechatronic systems. Proc. ASME 2003 Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., pp. 413422, Chicago, September 2–6.
Karayanakis, N. (1995). Advanced System Modelling and Simulation With Block Diagram Languages. Boca Raton, FL: CRC Press.
Karnopp, D., Margolis, D., & Rosenberg, R. (1990). System Dynamics: A Unified Approach. New York: Wiley.
Kruse, B., Gilz, T., Shea, K., & Eigner, M. (2014). Systematic comparison of functional models in SysML for design library evaluation. Procedia CIRP 21, 3439.
Kruse, B., Munzer, C., Wolkl, S., Canedo, A., & Shea, K., (2012). A model-based functional modeling and library approach for mechatronic systems in SysML. Proc. 32nd Computers and Information in Engineering Conf., Parts A and B, Vol. 2., pp. 12171227. New York: ASME.
Kruse, B., & Shea, K. (2016). Design library solution patterns in SysML for concept design and simulation. Procedia CIRP 50, 695700.
Kypuros, J. (2013). System Dynamics and Control with Bond Graph Modeling. Boca Raton, FL: CRC Press.
Linsey, J.S., Markman, A.B., & Wood, K.L. (2012). Design by analogy: a study of the WordTree method for problem re-representation. Journal of Mechanical Design 134, 112.
Linsey, J.S., Tseng, I., Fu, K., Cagan, J., Wood, K.L., & Schunn, C. (2010). A study of design fixation, its mitigation and perception in engineering design faculty. Journal of Mechanical Design 13, 41.
Lopez, R., Linsey, J.S., & Smith, S.M. (2011). Characterizing the effect of domain distance in design-by-analogy. Proc. 23rd Int. Conf. Design Theory and Methodology, pp. 141151, Washington, DC, August 28–31.
Lucero, B.M. (2014). Design-analogy performance parameter system (D-APPS) . PhD Thesis. Colorado School of Mines.
Lucero, B., Linsey, J., & Turner, C.J. (2016). Frameworks for organising design performance metrics. Journal of Engineering Design 27(4–6), 175204.
Lucero, B., Viswanathan, V., Linsey, J., & Turner, C. (2014). Identifying critical functions for use across engineering design domains. Journal of Mechanical Design 136(12), 121101.
Mayr, O. (1970). The origins of feedback control. Scientific American 223(4), 110118.
Mcbride, R.T., & Cellier, F.E. (2001). A bond-graph representation of a two-gimbal gyroscope. Simulation Series 33, 305312.
Montecchi, T., & Russo, D. (2011). FBOS: function/behaviour-oriented search. Proc. 11th ETRIA World TRIZ, Dublin, Ireland.
Nagel, R.L. (2007). Signal flow grammar from the functional basis. Proc. Int. Conf. Engineering Design, Paris, August 28–31.
Nagel, R.L., Vucovich, J.P., Stone, R. B., & McAdams, D. A. (2008). A signal grammar to guide functional modeling of electromechanical products. Journal of Mechanical Design 130(5), 51.
Otto, K.N., & Wood, K.L. (2001). Product Design: Techniques in Reverse Engineering and New Product Development. Englewood Cliffs, NJ: Prentice Hall.
Pahl, G., & Beitz, W. (1961). Engineering Design: A Systematic Approach. London: Design Council.
Qian, L., & Gero, J.S. (1996). Function-behavior-structure paths and their role in analogy-based design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10, 289312.
Russo, D. (2012). Functional-based search for patent technology transfer. Proc. ASME 2012, pp. 111, Chicago, August 12–15.
Russo, D., & Rizzi, C. (2014). A function oriented method for competitive technological intelligence and technology forecasting. Proc. 2014 Int. Conf. Engineering, Technology and Innovation, pp. 19, Bergamo, Italy, June 23–25.
Sonntag, R.E., Borgnakke, C., & Van Wylen, G.J. (2002). Fundamentals of Thermodynamics. New York: Wiley.
Westheimer, G. (1954). Eye movement responses to a horizontally moving visual stimulus. AMA Archives of Ophthalmology 52(6), 932941.
White, F. (2003). Fluid Mechanics, 6th ed. New York: McGraw-Hill.
Wu, Z., Campbell, M.I., & Fernández, B.R. (2008). Bond graph based automated modeling for computer-aided design of dynamic systems. Journal of Mechanical Design 130(4), 41102.
Yuan, Z., & Ljung, L. (2016). Black-box identification of multivariable transfer functions—asymptotic properties and optimal input design. Internaltional Journal of Control 40(2), 233256.

Keywords

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed