Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-10T14:38:38.987Z Has data issue: false hasContentIssue false
  • English
  • Français

Improving the link between computer-assisted design and configuration tools for the design of mechanical products

Published online by Cambridge University Press:  15 January 2013

Roberto Raffaeli ,
Maura Mengoni  and
Michele Germani
Roberto Raffaeli*
Affiliation:
Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, Italy
Maura Mengoni
Affiliation:
Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, Italy
Michele Germani
Affiliation:
Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, Italy
*
Reprint requests to: Roberto Raffaeli, Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, Via Brecce Bianche, 12-60131 Ancona, Italy. E-mail: r.raffaeli@univpm.it
Get access Rights & Permissions [Opens in a new window]

Abstract

The competitive market forces companies to offer tailored products to meet specific customer needs. To avoid wasting time, design efforts generally address the configuration of existing solutions, without producing substantial design modifications. Configuration tools are used to achieve customized products starting from a common platform. Many approaches have been successfully proposed in literature to configure products. However, in the mechanical field they need further investigation in order to be efficiently linked to computer-aided design technologies. Research is focused on tools and methods to automatically produce geometrical models and improve the flexibility of the continuous product updating process. In this context, this paper aims to combine product configuration approaches with design automation techniques in order to support design activities of products to fulfill specific requirements. The approach is based on entities called configurable virtual prototypes. Three different domains are managed and connected via configurable virtual prototypes: product specifications, geometrical data, and product knowledge. In particular, geometry recognition rules are used to identify the parameterization of parts and the assembly mating constraints. The approach is exemplified through an industrial case study where a tool has been developed on the basis of the described method. Advantages of the system are shown in terms of achieved product configuration efficiency.

Keywords

Design AutomationGeometrical KnowledgeModularityProduct Configuration
Type
Regular Articles
Information
AI EDAM , Volume 27 , Issue 1 , February 2013 , pp. 51 - 64
Copyright
Copyright © Cambridge University Press 2013

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

Agarwal, M., Cagan, J., & Stiny, G. (2000). A micro language: generating MEMS resonators using a coupled form–function shape grammar. Environment and Planning B: Planning and Design 27, 615–626.CrossRefGoogle Scholar
Anselma, L., Magro, D., & Torasso, P. (2003). Automatically decomposing configuration problems. In AI*IA 2003: Advances in Artificial Intelligence (Cappelli, A., & Turini, F., Eds.), LNCS, Vol. 2829, pp. 39–52. Berlin: Springer.CrossRefGoogle Scholar
Ardissono, L., Felfernig, A., Friedrich, G., Goy, A., Jannach, D., Petrone, G., Schäfer, R., & Zanker, M. (2003). A framework for the development of personalized, distributed web-based configuration systems. AI Magazine 24(3), 93–108.Google Scholar
Bermell-Garcìa, P., Fan, I.-S., Li, G., Porter, R., & Butter, D. (2001). Effective abstraction of engineering knowledge for KBE implementation. Proc. Int. Conf. Engineering Design, ICED'01, Glasgow, August 21–23.Google Scholar
Bodein, Y., Rose, B., & Caillaud, E. (2009). Improving CAD performance: a decisional model for knowledgeware implementation. Proc. Int. Conf. Engineering Design, ICED'09. Stanford, CA: Design Society.Google Scholar
Browining, T. (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
Colombo, G., Girotti, A., & Rovida, E. (2005). Automatic design of a press brake for sheet metal bending. Proc. Int. Conf. Engineering Design, ICED'05. Melbourne: Design Society.Google Scholar
Felfernig, A. (2007). Standardized configuration knowledge representations as technological foundation for mass customization. IEEE Transactions on Engineering Management 54(1), 41–56.CrossRefGoogle Scholar
Felfernig, A., Friedrich, G., Jannach, D., Stumptner, M., & Zanker, M. (2003). Configuration knowledge representations for Semantic Web applications. Artificial Intelligence for Engineering, Design, Analysis and Manufacturing 17(1), 31–50.CrossRefGoogle Scholar
Fujita, K. (2002). Product variety optimisation under modular architecture. Computer Aided Design 34, 953–965.CrossRefGoogle Scholar
Germani, M., & Mandorli, F. (2004). Self-configuring components approach to product variant development. Artificial Intelligence for Engineering, Design, Analysis and Manufacturing 18(1), 41–54.CrossRefGoogle Scholar
Germani, M., Raffaeli, R., Bonaventura, L., & Mandorli, F. (2005). Development of a collaborative product development tool for plants design. Proc. Int. Conf. Engineering Design, ICED'05. Melbourne: Design Society.Google Scholar
Gonzalez-Zugasti, J.P., & Otto, K.N. (2000). Modular platform-based product family design. Proc. ASME 2000 Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2000/DAC-14283, Baltimore, MD, September 10–13.CrossRefGoogle Scholar
Gunter, A., & Kuhn, C. (1999). Knowledge-based configuration—survey and future directions. In XPS-99: Knowledge-Based Systems. Survey and Future Directions (Puppe, F., Ed.), LNCS, Vol. 1570, pp. 44–66.Google Scholar
Heisserman, J., Callahan, S., & Mattikalli, R. (2000). A design representation to support automated design generation. In Artificial Intelligence in Design (Gero, J.S., Ed.), pp. 545–566. Dordrecht: Kluwer.Google Scholar
Hulubei, T., Fruder, E.C., & Wallace, R.J. (2003). The Goldilocks problem. Artificial Intelligence for Engineering, Design, Analysis and Manufacturing 17(1), 3–11.CrossRefGoogle Scholar
Ishino, Y., & Jin, Y. (2002). Acquiring engineering knowledge from design processes. Artificial Intelligence for Engineering, Design, Analysis and Manufacturing 16(2), 73–91.CrossRefGoogle Scholar
Jiao, J., & Tseng, M.M. (1999). A methodology of developing product family architecture for mass customization. Journal of Intelligent Manufacturing 10, 3–20.CrossRefGoogle Scholar
Khalid, H.M., & Oon, Y.B. (2003). Usability of design by customer websites. In The Customer Centric Enterprise—Advances in Mass Customization and Personalization (Tseng, M.M., & Piller, F.T., Eds.), pp. 283–300. Berlin: Springer–Verlag.Google Scholar
Lin, Y.S., Shea, K., Johnson, A., Coultate, J., & Pears, J. (2009). A method and software tool for automated gearbox synthesis. Proc. ASME 2009 Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2009-86935, San Diego, CA, August 30–September 2.CrossRefGoogle Scholar
Lindemann, U., Maurer, M., & Braun, T. (2009). Structural Complexity Management: An Approach for the Field of Product Design, pp. 21–42. Berlin: Springer–Verlag.CrossRefGoogle Scholar
Martin, M.V., & Ishii, K. (2002). Design for variety: developing standardized and modularized product platform architectures. Journal of Research in Engineering Design 13, 213–235.CrossRefGoogle Scholar
McCormack, J.P., Cagan, J., & Vogel, C.M. (2004). Speaking the Buick language: capturing, understanding, and exploring brand identity with shape grammars. Design Studies 25, 1–29.CrossRefGoogle Scholar
Mittal, S., & Frayman, F. (1989). Towards a generic model of configuration tasks. Proc. 11th Int. Joint Conf. Artificial Intelligence, pp. 1395–1401. San Mateo, CA: Morgan Kaufmann.Google Scholar
Raffaeli, R., Cicconi, P., Mengoni, M., & Germani, M. (2010). Modular product configuration: an automatic tool for eliciting design knowledge from parametric CAD models. Proc. ASME 2010 Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2010-28242, Montreal, August 15–18.CrossRefGoogle Scholar
Raffaeli, R., Mengoni, M., Germani, G., & Mandorli, F. (2009). An approach to support the implementation of product configuration tools. Proc. ASME 2009 Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2009-86752, San Diego, CA, August 30–September 2.CrossRefGoogle Scholar
Roach, G.M. (2003). The product design generator—a next generation approach to detailed design. Doctoral Thesis. Brigham Young University.Google Scholar
Roach, G.M., Cox, J.J., & Young, J.M. (2003). A new strategy for automating the generation of product family members and artifacts to an aerospace application. Proc. ASME 2003 Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. CIE-2003-85, Chicago, September 2–6.CrossRefGoogle Scholar
Sabin, D., & Weigel, R. (1998). Product configuration frameworks—a survey. IEEE Intelligent Systems and Their Applications 13(4), 42–49.CrossRefGoogle Scholar
Saitou, K., Kazuhiro, I., Shinji, N., & Papalambros, P. (2005). A survey of structural optimization in mechanical product development. Journal of Computing and Information Science in Engineering 5, 214–226.CrossRefGoogle Scholar
Salehi, V., & McMahon, C. (2009a). Action research into the use of parametric associative CAD systems in an industrial context. Proc. Int. Conf. Engineering Design, ICED'09. Stanford, CA: Design Society.Google Scholar
Salehi, V., & McMahon, C. (2009b). Development of a generic integrated approach for parametric associative CAD systems. Proc. Int. Conf. Engineering Design, ICED'09. Stanford, CA: Design Society.Google Scholar
Schotborgh, W., Kokkeler, F., Tragter, H., & Van Houten, F. (2009). Why is design automation software not everywhere? Proc. Int. Conf. Engineering Design, ICED'09. Stanford, CA: Design Society.Google Scholar
Simpson, T.W., Siddique, Z., & Jiao, J. (2006). Product Platform and Product Family Design—Methods and Applications. New York: Springer Science + Business Media.CrossRefGoogle Scholar
Steger-Jensen, K., & Svensson, C. (2004). Issues of mass customisation and supporting IT-solutions. Computers in Industry 54(1), 83–103.CrossRefGoogle Scholar
Todeti, S.R., & Chakrabarti, A. (2009). An empirical model of the process synthesis of multiple state mechanical devices. Proc. Int. Conf. Engineering Design, ICED'09. Stanford, CA: Design Society.Google Scholar
Tseng, T., & Huang, C. (2008). Design support systems: a case study of modular design of the set-top box from design knowledge externalization perspective. Decision Support Systems 44(4), 909–924.CrossRefGoogle Scholar
Wang, G. (2002). Definition and review of virtual prototyping. Journal of Computing and Information Science in Engineering 2(3), 232–236.CrossRefGoogle Scholar
Yassine, A., Whitney, D., Daleiden, S., & Lavine, J. (2003). Connectivity maps: modeling and analysing relationships in product development processes. Journal of Engineering Design 14(3), 377–394.CrossRefGoogle Scholar
Zha, X., & Sriram, R. (2006). Platform-based product design and development: a knowledge intensive support approach. Knowledge-Based Systems 19, 524–543.CrossRefGoogle Scholar
Zorrassiatine, F., Wykes, C., Parkin, R., & Gindy, N. (2003). A survey of virtual prototyping techniques for mechanical product development. Journal of Engineering Manufacture 217(4), 513–530.CrossRefGoogle Scholar