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Identifying requirements for physics-based reasoning on function structure graphs

Published online by Cambridge University Press:  24 July 2013

Chiradeep Sen  and
Joshua D. Summers
Chiradeep Sen
Affiliation:
School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, Oregon, USA
Joshua D. Summers*
Affiliation:
Department of Mechanical Engineering, Clemson University, Clemson, South Carolina, USA
*
Reprint requests to: Joshua D. Summers, Department of Mechanical Engineering, Clemson University, 250 Fluor Daniel Building, Clemson, SC 29634-0921, USA. E-mail: joshua.summers@ces.clemson.edu
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Abstract

Function-based design and modeling have been taught, studied, and practiced in various forms for several years with efforts centered on using function modeling to help designers understand problems or to facilitate idea generation. Only limited focus has been placed on potential use for qualitative and quantitative reasoning and analysis of the design concept. This potential for early stage analysis has not been fully explored partly because computational reasoning tools have not been developed for this express purpose. This paper presents a set of requirements and their justification to realize this design enabling tool. The requirements include coverage, consistency, validity against physics laws, domain neutrality, physics-based definitions, normative and descriptive modeling, and qualitative and quantitative modeling and reasoning. Each requirement is defined in concrete terms and illustrated with examples and logic. With the requirements for function-based reasoning and representation clearly identified, future research toward formalizing of function-based design will be more focused and objective validation of proposed representations against these requirements would be possible.

Keywords

Concept AnalysisConceptual DesignFormal RepresentationFunction ModelingReasoning
Type
Response Papers
Information
AI EDAM , Volume 27 , Issue 3: Functional Descriptions in Engineering , August 2013 , pp. 291 - 299
Copyright
Copyright © Cambridge University Press 2013 

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References

REFERENCES

Albers, A., Thau, S., & Alink, T. (2008). Support of design engineering activity through C & CM- temporal decomposition of design problems. Proc. Tools and Methods for Competitive Engineering Conf., pp. 295–306, Izmir, Turkey.Google Scholar
Baader, F. (1996). A formal definition for the expressive power of terminological knowledge representation languages. Journal of Logic and Computation 6(1), 33–54.CrossRefGoogle Scholar
Bergmann, M., Moor, J., & Nelson, J. (1990). The Logic Book. New York: McGraw–Hill.Google Scholar
Bohm, M., Stone, R., & Szykman, S. (2005). Enhancing virtual product representations for advanced design repository systems. ASME Transactions Journal of Computing and Information Science in Engineering 5(4), 360–372.CrossRefGoogle Scholar
Cebrian-Tarrason, D., Lopez-Montero, J.A., & Vidal, R. (2008). OntoFaBeS: ontology design based in FBS framework. Proc. CIRP Design Conference: Design Synthesis. Enschede, Netherlands: CIRP.Google Scholar
Chakrabarti, A., & Bligh, T.P. (2001). A scheme for functional reasoning in conceptual design. Design Studies 22(6), 493–517.CrossRefGoogle Scholar
Chandrasekaran, B. (2005). Representing function: relating functional representation and functional modeling research streams. Artificial Intelligence in Engineering Design, Analysis and Manufacturing 19, 65–74.Google Scholar
Chandrasekaran, B., & Josephson, J. (2000). Function in device representation. Engineering With Computers 16(3–4), 162–177.CrossRefGoogle Scholar
Chavali, S.R.K., Sen, C., Mocko, G.M., & Summers, J.D. (2008). Using rule-based design in engineer-to-order industry: an SME case study. Computer-Aided Design and Applications 5(1–4), 178–193.CrossRefGoogle Scholar
Collins, J.A., Hagan, B.T., & Bratt, H.M. (1976). The failure-experience matrix—a useful design tool. Journal of Engineering for Industry 98(3), 1074.CrossRefGoogle Scholar
De Kleer, J., & Brown, J.S. (1984). A qualitative physics based on confluences. Artificial Intelligence 24(1), 7–83.CrossRefGoogle Scholar
Deng, Y.M. (2002). Function and behavior representation in conceptual mechanical design. Artificial Intelligence in Engineering Design, Analysis and Manufacturing 16(5), 343–362.CrossRefGoogle Scholar
Eastman, C.M. (1969). Cognitive processes and ill-defined problems: a case study from design. Proc. Joint Int. Conf. Artificial Intelligence, pp. 669–690. San Francisco, CA: Morgan Kaufmann.Google Scholar
Eckert, C. (2013). That which is not form: the practical challenges in using functional concepts in design. Artificial Intelligence in Engineering Design, Analysis and Manufacturing 27(3), 217–232 [this issue].CrossRefGoogle Scholar
Fenves, S.J., Foufou, S., Bock, C., & Sriram, R.D. (2008). CPM2: a core model for product data. Journal of Computing and Information Science in Engineering 8, 14501.CrossRefGoogle Scholar
Forbus, K.D. (1984). Qualitative process theory. Artificial Intelligence 24(1), 85–168.CrossRefGoogle Scholar
Freeman, P., & Newell, A. (1971). A model for functional reasoning in design. Proc. 2nd Int. Joint Conf. Artificial Intelligence, pp. 621–640. San Francisco, CA: Morgan Kaufmann.Google Scholar
Garbacz, P. (2005). Towards a standard taxonomy of artifact functions. Applied Ontology 1, 221–236.Google Scholar
Gero, J., & Kannengiesser, U. (2002). The situated–function–behaviour–structure framework. In Artificial Intelligence in Design (Gero, J., Ed.), pp. 89–104. Norwell, MA: Kluwer Academic.Google Scholar
Goel, A.K. (2013). One 30-year case study and 15 principles: implications of an artificial intelligence methodology for functional modeling. Artificial Intelligence in Engineering Design, Analysis and Manufacturing 27(3), 203–215 [this issue].CrossRefGoogle Scholar
Goel, A.K., & Bhatta, S.R. (2004). Use of design patterns in analogy-based design. Advanced Engineering Informatics 18, 85–94.CrossRefGoogle Scholar
Goel, A.K., Bhatta, S.R., & Stroulia, E. (1997). KRITIK: an early case-based design system. In Issues and Applications of Case-Based Reasoning in Design (Maher, M.L. & Pu, P., Eds.), pp. 87–132. Mahwah, NJ: Erlbaum.Google Scholar
Hirtz, J., Stone, R., McAdams, D., Szykman, S., & Wood, K. (2002). A functional basis for engineering design: reconciling and evolving previous efforts. Research in Engineering Design 13, 65–82.CrossRefGoogle Scholar
Hubka, V., & Eder, W.E. (1988). Theory of Technical Systems: A Total Concept Theory for Engineering Design. Berlin: Springer–Verlag.CrossRefGoogle Scholar
Kim, S., & Duffy, A.H.B. (2003). Towards an ontology of generic engineering design activities. Research in Engineering Design 14, 200–223.Google Scholar
Kirschman, C.F., & Fadel, G.M. (1998). Classifying functions for mechanical design. Journal of Mechanical Design 120(3), 475–482.CrossRefGoogle Scholar
Kitamura, Y., Kashiwase, M., Fuse, M., & Mizoguchi, R. (2004). Deployment of an ontological framework of functional design knowledge. Advanced Engineering Informatics 18(2), 115–127.CrossRefGoogle Scholar
Kroes, P. (2010). Formalization of technical functions: why is it so difficult. Proc. Tools and Methods for Competitive Engineering Conf. (TMCE 2010), Ancona, Italy, April 12–16.Google Scholar
Kurtoglu, T., Swantner, A., & Campbell, M. (2010). Automating the conceptual design process: from black box to component selection. Artificial Intelligence in Engineering Design, Analysis and Manufacturing 24(1), 49–62.CrossRefGoogle Scholar
Lind, M. (1994). Modeling goals and functions of complex industrial plants. Applied Artificial Intelligence an International Journal 8(2), 259–283.CrossRefGoogle Scholar
Linz, P. (2011). An Introduction to Formal Languages and Automata (5th ed.). Sudberry, MA: Jones & Bartlett.Google Scholar
Luger, G.F. (2005). Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Essex: Addison–Wesley Longman.Google Scholar
Maier, J.R.A., & Fadel, G.M. (2009). Affordance-based design methods for innovative design, redesign and reverse engineering. Research in Engineering Design 20(4), 225–239.CrossRefGoogle Scholar
McAdams, D., & Wood, K. (2002). A quantiative similarity metric for design-by-analogy. ASME Transactions Journal of Mechanical Design 124(2), 173–182.CrossRefGoogle Scholar
Nagel, R.L., Perry, K.L., Stone, R.B., & McAdams, D.A. (2009). FunctionCAD: a functional modeling application based on the function design framework. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., San Diego, CA.CrossRefGoogle Scholar
Otto, K., & Wood, K. (2001). Product Design Techniques in Reverse Engineering and New Product Development. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Pahl, G., Beitz, W., Wallace, K., & Blessing, L. (2007). Engineering Design: A Systematic Approach (3rd ed.). London: Springer–Verlag.CrossRefGoogle Scholar
Rodenacker, W. (1971). Methodisches Konstruieren. Berlin: Springer–Verlag.Google Scholar
Sasajima, M., Kitamura, Y., Ikeda, M., & Mizoguchi, R. (1995). FBRL: a function and behavior representation language. Proc. Int. Joint Conf. Artificial Intelligence, pp. 1830–1836. San Francisco, CA: Morgan Kaufmann.Google Scholar
Sen, C. (2012). A formal representation of mechanical functions to support physics-based computational reasoning in early mechanical design. PhD Thesis. Clemson University.Google Scholar
Sen, C., Summers, J.D., & Mocko, G.M. (2011 a). A protocol to formalise function verbs to support conservation-based model checking. Journal of Engineering Design 22 (11–12), 765–788.CrossRefGoogle Scholar
Sen, C., Summers, J.D., & Mocko, G.M. (2011 b). Exploring potentials for conservational reasoning using topologic rules of function structure graphs. Proc. 18th Int. Conf. Engineering Design (ICED11), Vol. 9, pp. 377–388. Copenhagen: Design Society.Google Scholar
Sen, C., Summers, J.D., & Mocko, G.M. (2013). A formal representation of function structure graphs for computer-directed modeling and conservation-based reasoning. ASME Transactions Journal of Computing and Information Science in Engineering 13(1).Google Scholar
Simon, H. (1998). The Sciences of the Artificial. Cambridge, MA: MIT Press.Google Scholar
Sridharan, P., & Campbell, M. (2005). A study on the grammatical construction of function structures. Artificial Intelligence in Engineering Design, Analysis and Manufacturing 19(3), 139–160.CrossRefGoogle Scholar
Srinivasan, V., Chakrabarti, A., & Lindemann, U. (2012). A framework for describing functions in design. Proc. Int. Design Conf., pp. 1111–1122. Dubrovnik: Design Society.Google Scholar
Stone, R.B., Tumer, I.Y., & Van Wie, M. (2005). The function-failure design method. ASME Transactions Journal of Mechanical Design 127(3), 397–407.CrossRefGoogle Scholar
Summers, J.D. (2005 a). Expressiveness of the design exemplar. Proc. Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. CIE-85135. Long Beach, CA: ASME.Google Scholar
Summers, J.D. (2005 b). Reasoning in engineering design. Proc. Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DTM-85334. Long Beach, CA: ASME.CrossRefGoogle Scholar
Summers, J.D., & Shah, J.J. (2004). Representation in engineering design: a framework for classification. Proc. Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DTM-57514, Salt Lake City, UT.CrossRefGoogle Scholar
Thomke, S., & Fujimoto, T. (2000). The effect of “front-loading” problem-solving on product development performance. Journal of Product Innovation Management 17(2), 128–142.CrossRefGoogle Scholar
Ullman, D.G. (2010). The Mechanical Design Process. New York: McGraw–Hill.Google Scholar
Umeda, Y., Ishii, M., Yoshioka, M., Shimomura, Y., & Tomiyama, T. (1996). Supporting conceptual design based on the function–behavior–state modeler. Artificial Intelligence in Engineering Design, Analysis and Manufacturing 10(4), 275–288.CrossRefGoogle Scholar
Van Eck, D., McAdams, D.A., & Vermaas, P.E. (2007). Functional decomposition in engineering: a survey. Proc. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Las Vegas, NV.Google Scholar
Vermaas, P.E. (2013). The coexistence of engineering meanings of function: four responses and their methodological implications. Artificial Intelligence in Engineering Design, Analysis and Manufacturing 27(3), 191–202 [this issue].CrossRefGoogle Scholar
Vescovi, M., Iwasaki, Y., Fikes, R., & Chandrasekaran, B. (1993). CFRL: a language for specifying the causal functionality of engineered devices. Proc. 11th National Conf. Artificial Intelligence. Washington, DC: American Association for Artificial Intelligence.Google Scholar
Vucovich, J., Bhardwaj, N., Hoi-Hei, H., Ramakrishna, M., Thakur, M., & Stone, R. (2006). Concept generation algorithms for repository-based early design. Proc. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2006–99466, Philadelphia, PA.CrossRefGoogle Scholar
Wang, C., Qing-liang, Z., & Albers, A. (2009). A unified product model based on C&CM for mechatronic product development. Proc. Int. Conf. Mechatronics and Automation, ICMA 2009, pp. 2173–2177, Changchun, Jilin, China, August 9–12.Google Scholar