Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-20T00:20:12.622Z Has data issue: false hasContentIssue false
  • English
  • Français

A formal functional representation methodology for conceptual design of material-flow processing devices

Published online by Cambridge University Press:  04 October 2016

Yong Chen ,
Meng Zhao ,
Ying Liu  and
Youbai Xie
Yong Chen*
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
Meng Zhao
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
Ying Liu
Affiliation:
Institute of Mechanical and Manufacturing Engineering, Cardiff University, Cardiff, United Kingdom
Youbai Xie
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
*
Reprint requests to: Yong Chen, School of Mechanical Engineering, Room 801, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Minhang District, Shanghai 200240, China. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Although there has been considerable computer-aided conceptual design research, most of the proposed approaches are domain specific and can merely achieve conceptual design of energy flows-processing systems. Therefore, this research is devoted to the development of a general (i.e., domain-independent) and knowledge-based methodology that can search in a wide multidisciplinary solution space for suitable solution principles for desired material-flow processing functions without designers' biases toward familiar solution principles. It first proposes an ontology-based approach for representing desired material-flow processing functions in a formal and unambiguous manner. Then a rule-based approach is proposed to represent the functional knowledge of a known solution principle in a general and flexible manner. Thereafter, a simulation-based retrieval approach is developed, which can search for suitable solution principles for desired material-flow processing functions. The proposed approaches have been implemented as a computer-aided conceptual design system for test. The conceptual design of a coin-sorting device demonstrates that our functional representation methodology can make the proposed computer-aided conceptual design system to effectively and precisely retrieve suitable solution principles for a desired material-flow processing function.

Keywords

Conceptual DesignFunctionMaterial-Flow Processing DevicesSolution Principle
Type
Special Issue Articles
Information
AI EDAM , Volume 30 , Special Issue 4: Engineering Design Informatics , November 2016 , pp. 353 - 366
Copyright
Copyright © Cambridge University Press 2016 

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

Arnold, C.R.B., Stone, R.B., & McAdams, D.A. (2008). MEMIC: an interactive morphological matrix tool for automated concept generation. Proc. Int. Industrial Engineering Annual Conf. Norcross: Institute of Industrial Engineers.Google Scholar
Bunge, M. (1977). Treatise on Basic Philosophy: Ontology I. The Furniture of the World. Dordrecht: D. Reidel.Google Scholar
Cagan, J., Campbell, M.I., Finger, S., & Tomiyama, T. (2005). A framework for computational design synthesis. ASME Journal of Computing and Information Science in Engineering 5(3), 171–181.CrossRefGoogle Scholar
Campbell, M.I., Cagan, J., & Kotovsky, K. (2000). Agent-based synthesis of electromechanical design configurations. ASME Journal of Mechanical Design 122(1), 61–69.Google Scholar
Chakrabarti, A., & Bligh, T.P. (1996). An approach to functional synthesis of mechanical design concepts: theory, applications, and emerging research issues. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10(4), 313–331.Google Scholar
Chandrasekaran, B. (1994). Functional representation: a brief historical perspective. Applied Artificial Intelligence 8(2), 173–197.CrossRefGoogle Scholar
Chandrasekaran, B., Josephson, J.R., & Benjamins, V.R. (1999). What are ontologies, and why do we need them? IEEE Intelligent Systems 14(1), 20–26.CrossRefGoogle Scholar
Chen, Y., Feng, P., He, B., Xie, Y., & Lin, Z. (2006). Automated conceptual design of mechanisms based on improved morphological matrix. ASME Journal of Mechanical Design 128(3), 516–526.Google Scholar
Chen, Y., Lin, Z., & Feng, P. (2005). A genetics-based approach for the principle conceptual design of mechanical products. International Journal of Advanced Manufacturing Technology 27(3–4), 225–233.CrossRefGoogle Scholar
Chen, Y., Zhang, Z., Huang, J., & Xie, Y. (2013). Towards a scientific ontology-based concept of function. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27(3), 241–248.Google Scholar
Chen, Y., Zhang, Z., Xie, Y., & Zhao, M. (2015). A new model of conceptual design based on scientific ontology and intentionality theory: Part I. The conceptual foundation. Design Studies 37, 12–36.Google Scholar
Deng, Y.M., Britton, G.A., & Tor, S.B. (2000). Constraint-based functional design verification for conceptual design. Computer-Aided Design 32(14), 889–899.Google Scholar
Eisenbart, B., Gericke, K., & Blessing, L. (2013). An analysis of functional modeling approaches across disciplines. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27(3), 281–289.Google Scholar
Feng, P., Xu, G., & Zhang, M. (1996). Feature modeling based on design catalogues for principle conceptual design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10(4), 347–354.Google Scholar
Gero, J.S. (1990). Design prototypes: a knowledge representation schema for design. AI Magazine 11(4), 26–36.Google Scholar
Goel, A.K. (2013). A 30-year case study and 15 principles: implications of an artificial intelligence methodology for functional modelling. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27(3), 203–215.Google Scholar
Gruber, T.R. (1995). Toward principles for the design of ontologies used for knowledge sharing? International Journal of Human-Computer Studies 43(5), 907–928.CrossRefGoogle Scholar
Gu, C.C., Hu, J., Peng, Y.H., & Li, S. (2012). FCBS model for functional knowledge representation in conceptual design. Journal of Engineering Design 23(8), 577–596.Google Scholar
Helms, B., Schultheiss, H., & Shea, K. (2013). Automated mapping of physical effects to functions using abstraction ports based on bond graphs. ASME Journal of Mechanical Design 135(5), 051006.Google Scholar
Hirtz, J., Stone, R.B., McAdams, D.A., Szykman, S., & Wood, K.L. (2002). A functional basis for engineering design: reconciling and evolving previous efforts. Research in Engineering Design 13(2), 65–82.Google Scholar
Kolodner, J. (1993). Case-Based Reasoning. San Mateo, CA: Morgan Kaufmann.Google Scholar
Kuipers, B. (1986). Qualitative simulation. Artificial Intelligence 29(3), 289–338.CrossRefGoogle Scholar
Kurtoglu, T., Campbell, M.I., Arnorld, C.B., Stone, R.B., & McAdams, D.A. (2009). A component taxonomy as a framework for computational design synthesis. ASME Journal of Computing and Information Sciences in Engineering 9(1), 011007.Google Scholar
Kurtoglu, T., Swantner, A., & Campbell, M.I. (2010). Automating the conceptual design process: from black box to component selection. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24(1), 49–62.Google Scholar
Lenat, D.B. (1989). Ontological versus knowledge engineering. IEEE Transactions on Knowledge and Data Engineering 1(1), 84–88.Google Scholar
Li, S., Hu, J., & Peng, Y.H. (2010). Representation of functional micro-knowledge cell (FMKC) for conceptual design. Engineering Applications of Artificial Intelligence 23(4), 569–585.Google Scholar
Miles, L.D. (1972). Techniques of Value Analysis and Engineering. New York: McGraw-Hill.Google Scholar
Pahl, G., & Beitz, W. (2007). Engineering Design: A Systematic Approach, 3rd ed. London: Springer–Verlag.Google Scholar
Prabhakar, S., & Goel, A.K. (1998). Functional modeling for enabling adaptive design of devices for new environments. Artificial Intelligence in Engineering 12(4), 417–444.Google Scholar
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(4), 289–312.Google Scholar
Rosenberg, R., & Karnopp, D. (1983). Introduction to Physical Systems Dynamics. New York: McGraw-Hill.Google Scholar
Schmidt, L.C., & Cagan, J. (1997). GGREADA: a graph grammar-based machine design algorithm. Research in Engineering Design 9(4), 195–213.CrossRefGoogle Scholar
Sen, C., Summers, J.D., & Mocko, G.M. (2013). Physics-based reasoning in conceptual design using a formal representation of functional structure graphs. ASME Journal of Computing and Information Science in Engineering 13(1), 011008.CrossRefGoogle Scholar
Sridharan, P., & Campbell, M.I. (2005). A study on the grammatical construction of function structures. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19(3), 139–160.Google Scholar
Stone, R.B., Tumer, I.Y., & Stock, M.E. (2005). Linking product functionality to historic failures to improve failure analysis in design. Research in Engineering Design 16(2), 96–108.Google Scholar
Stone, R.B., & Wood, K.L. (2000). Development of a functional basis for design. ASME Journal of Mechanical Design 122(4), 359–370.Google Scholar
Sturges, R.H., O'Shaughnessy, K., & Kilani, M.I. (1996). Computational model for conceptual design based on extended function logic. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10(4), 255–274.Google Scholar
Szykman, S., Racz, J., Bochenek, C., & Sriram, R.D. (2000). A web-based system for design artefact modelling. Design Studies 21, 145–165.Google Scholar
Ulrich, K.T., & Seering, W.P. (1989). Synthesis of schematic descriptions in mechanical design. Research in Engineering Design 1(1), 3–18.Google Scholar
Umeda, Y., Ishii, M., Yoshioka, M., Shimomura, Y., & Tomiyama, T. (1996). Supporting conceptual design based on the function–behavior–state modeller. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10(4), 275–288.CrossRefGoogle Scholar
Welch, R.V., & Dixon, J.R. (1994). Guiding conceptual design through behavioural reasoning. Research in Engineering Design 6(3), 169–188.Google Scholar
Wölkl, S., & Shea, K. (2009). A computational product model for conceptual design using SysML. Proc. ASME 2009 Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2009-87239, San Diego, CA, August 30–September 2.Google Scholar