Fuzzy Logic Applications in Computer Aided Design

Much research work has been devoted to developing techniques for improving the capabilities of computer aided design (CAD) systems for limited engineering and architectural purposes, where the domain of objects to be designed is somewhat restrictive and constrained in scope. Other types of design such as sculpting or industrial design, where aesthetic factors play a more important role, have not been adequately catered for. The main reason for this neglect is that aesthetic factors have fuzzy characteristics, are subjective and difficult to specify. This paper identifies the needs for fuzzy logic in the development of CAD systems, and in particular, discusses how fuzzy logic can be used to model aesthetic factors. We categorise aesthetic intents, analyse the requirements for their representations and present a systematic scheme to realise aesthetic intents using fuzzy logic. Finally, we show how this scheme can be applied to enrich the process of producing artistic work such as brush painting.

[1]  A. Ralescu,et al.  Simulation, Knowledge-Based Computing, and Fuzzy Statistics , 1987 .

[2]  Richard Coyne,et al.  Innovation and Creativity in Knowledge-based CAD , 1987 .

[3]  B. Pham.  Offset approximation of uniform B-splines , 1988 .

[4]  Tapio Takala,et al.  Industrial Design Based on Geometric Intentions , 1988 .

[5]  David R. Wallace,et al.  Automated product concept design: unifying aesthetics and engineering , 1993, IEEE Computer Graphics and Applications.

[6]  Alan H. Barr,et al.  Global and local deformations of solid primitives , 1984, SIGGRAPH.

[7]  E. Sittas,et al.  3D design reference framework , 1991, Comput. Aided Des..

[8]  Yun-Jie Pang,et al.  Combining computer graphics with Chinese traditional painting , 1987, Comput. Graph..

[9]  Ralph R. Martin,et al.  Cyclides in surface and solid modeling , 1993, IEEE Computer Graphics and Applications.

[10]  Sabine Coquillart,et al.  Axial deformations: an intuitive deformation technique , 1994, Comput. Aided Des..

[11]  Dimitris N. Metaxas,et al.  Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Christopher Tong,et al.  Artificial Intelligence in Engineering Design , 1992 .

[13]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[14]  Adrian Demaid,et al.  Object-oriented representation of qualitative engineering properties , 1994, Comput. Aided Des..

[15]  T. Smithers,et al.  AI-based versus geometry-based design or Why design cannot be supported by geometry alone , 1989 .

[16]  Binh Pham.  Expressive brush strokes , 1991, CVGIP Graph. Model. Image Process..

[17]  Duc Truong Pham,et al.  Artificial Intelligence in Design , 1991 .

[18]  Martti Mäntylä,et al.  Feature modelling by incremental feature recognition , 1993, Comput. Aided Des..

[19]  John S. Gero.  Expert Systems in Computer-Aided Design , 1987 .

[20]  Steve Strassmann,et al.  Hairy brushes , 1986, SIGGRAPH.

[21]  P.J.W. teHagen,et al.  Organization of design knowledge in an intelligent CAD environment , 1987 .

[22]  T. Kuragano,et al.  FRESDAM system for design of aesthetically pleasing free-form objects and generation of collision-free tool paths , 1992, Comput. Aided Des..

[23]  Ming C. Leu,et al.  Analysis and modelling of deformed swept volumes , 1994, Comput. Aided Des..

[24]  Andrew J. Hanson,et al.  Hyperquadrics: Smoothly deformable shapes with convex polyhedral bounds , 1988, Comput. Vis. Graph. Image Process..

[25]  Barr,et al.  Superquadrics and Angle-Preserving Transformations , 1981, IEEE Computer Graphics and Applications.

[26]  Sudhir P. Mudur,et al.  The Brush-Trajectory Approach to Figure Specification: Some Algebraic Solutions , 1984, TOGS.

[27]  Franca Giannini,et al.  Feature-based modelling by integrating design and recognition approaches , 1994, Comput. Aided Des..

[28]  张哉根,et al.  Leu-M , 1991 .