Full Article - Open Access.

Idioma principal

From Parametric to Meta Modeling in Design

Bernal, Marcelo ;

Full Article:

This study introduces the Meta-Modeling process adopted from the Model Based System Engineering field (MBSE) to explore an approach for the generation of design alternatives beyond the restrictions of the Parametric Models that mainly produce geometric variations and have limitations in terms of topological transformations during the exploratory design tasks. The Meta-Model is the model of attributes and relationships among objects of a particular domain. It describes objects and concepts in abstract terms independent from the complexity of the geometric models and provides mapping mechanisms that facilitate the interfacing with parametric parts. The flexibility of these computer-interpretable and human-readable models can contribute to creatively manipulate the design knowledge embedded in parametric models.

Full Article:

Palavras-chave: Parametric Modeling; Meta-Modeling; Model Based System Engineering; Modeling Languages; Systems Integration,

Palavras-chave: ,

DOI: 10.5151/despro-sigradi2016-815

Referências bibliográficas
  • [1] Bernal, M., & Eastman, C. (2011). Top-down Approach to Embed Design Expertise in Parametric Objects for the Automatic Generation of a Building Service Core. Paper presented at the CAAD Futures.
  • [2] Bernal, M., Haymaker, J. R., & Eastman, C. (2015). On the role of computational support for designers in action. Design Studies, 41, 163-18 doi:10.1016/j.destud.2015.08.001
  • [3] Bohnke, D., Reichwein, A., & Rudolph, S. (2009). Design language for airplane geometries using the unified modeling language. Paper presented at the ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference.
  • [4] Clevenger, C. M., & Haymaker, J. (2011). Metrics to assess design guidance. Design Studies, 32(5), 431-456.
  • [5] Eastman, C. (1999). Building product models: computer environments, supporting design and construction. Boca Raton, Florida: CRC press.
  • [6] Eastman, C., Sacks, R., & Lee, G. (2003). Development of a Knowledge-Rich CAD System for American Precast Concrete Industry. Paper presented at the Association for Computer Aided Design in Architecture (ACADIA), Muncie, Indiana.
  • [7] Eastman, C., Teicholz, P., Sacks, R. & Liston, K. (2011). BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers, and Contractors. (second ed.). Hoboken, NJ: John Wiley & Sons Inc.
  • [8] Eck, O. & Schaefer, D. (2011). A semantic file system for integrated product data management. Advanced Engineering Informatics, 25(2), 177-184.
  • [9] Frazer, J., Tang, M., & Sun, J. (1999). Towards a generative system for intelligent design support. Paper presented at the Proceedings of the 4th CAADRIA Conference.
  • [10] Friedenthal, S., Moore, A., & Steiner, R. (2011). A practical guide to SysML: the systems modeling language: Elsevier.
  • [11] Gentry, R., Sharif, S., Cavieres, A. & Bigg, D. (2016). BIM schema for masonry units and walls. Proceedings of the 16th International Brick and Block Masonry Conference, Padova, Italy, 26-30 June 2016
  • [12] Goel, A. & Chandrasekaran, B. (1992). Case-based design: A task analysis. Artificial intelligence approaches to engineering design, 2, 165-184.
  • [13] INCOSE. (2016, 01/12/2016). Retrieved from http://www.incose.org/
  • [14] Kalay, Y. E. (1989). Modeling objects and environments: Wiley.
  • [15] Kasik, D., Buxton, W., & Ferguson, D. (2005). Ten CAD challenges. IEEE Computer Graphics and Applications, 81-92.
  • [16] Kifer, M., Lausen, G., & Wu, J. (1995). Logical foundations of object-oriented and frame-based languages. Journal of the ACM (JACM), 42(4), 741-843.
  • [17] Kleijnen, J. P. (1997). Sensitivity analysis and related analyses: a review of some statistical techniques. Journal of Statistical Computation and Simulation, 57(1-4), 111-142.
  • [18] Kolodner, J. L. (1992). An introduction to case-based reasoning. Artificial Intelligence Review, 6(1), 3-34.
  • [19] Kühne, T. (2006). Matters of (meta-) modeling. Software & Systems Modeling, 5(4), 369-385.
  • [20] La Rocca, G. (2011). Knowledge based engineering techniques to support aircraft design and optimization: TU Delft, Delft University of Technology.
  • [21] Lawson, B. and K. Dorst (2009). "Design expertise." Recherche 67: 02.
  • [22] Lee, G., Sacks, R., & Eastman, C. (2006). Specifying Parametric Building Object Behavior (BOB) for a Building Information Modeling System. Automation in Construction, 15(6), 758-776.
  • [23] MagicDraw. (2016, 2016). Retrieved from http://www.nomagic.com/products/magicdraw.html
  • [24] Mantyla, M. (1988). An Introduction to Solid Modeling.
  • [25] Reichwein, A., & Paredis, C. J. (2011). Overview of Architecture Frameworks and Modeling Languages for Model-Based Systems Engineering. Paper presented at the Proceedings of the ASME 2011 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference.
  • [26] Gómez, P. & Swarts, M. (2014). Campus Information-and-knowledge Modeling: Embedding Multidisciplinary Knowledge into a Design Environment for University Campus Planning. International Journal of Architectural Computing 12(4): 439-457.
  • [27] Unified Modeling Language®. (2016, 02/01/2016 ). Retrieved from http://www.uml.org
Como citar:

Bernal, Marcelo; "From Parametric to Meta Modeling in Design", p. 579-583 . In: XX Congreso de la Sociedad Iberoamericana de Gráfica Digital [=Blucher Design Proceedings, v.3 n.1]. São Paulo: Blucher, 2016.
ISSN 2318-6968, DOI 10.5151/despro-sigradi2016-815

últimos 30 dias | último ano | desde a publicação


downloads


visualizações


indexações