Analysis of variability in the design of wood products under imprecision

The design and analysis of many products is performed with imprecisely known parameters, relationships, and environmental conditions. This is especially true for wood products which exhibit greater variability than most materials. Fuzzy set theory applied to the design and analysis of wood products is regarded as a promising approach for modeling the geometric and mechanical property variability. A fuzzy constraint model is used to analyze the design of a wood beam structure. The analysis is compared with a Monte-Carlo simulation and a root sum of squares analysis approach. The fuzzy set design approach compares favourably with these approaches and has several distinct advantages. It can model user preference as well as imprecision, it is computationally quick, and it better reduces the design space.

[1]  Ronald E. Giachetti,et al.  A methodology for the reduction of imprecision in the engineering process , 1997, Eur. J. Oper. Res..

[2]  Kevin Otto,et al.  Propagating imprecise engineering design constraints , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[3]  Kevin Otto,et al.  Extensions to the Taguchi method of product design , 1993 .

[4]  K. Muller,et al.  Applications of fuzzy hierarchies and fuzzy MADM methods to innovative system design , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[5]  H.-J. Zimmermann,et al.  Intelligent system design support by fuzzy-multi-criteria decision making and/or evolutionary algorithms , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[6]  Robert E. Young,et al.  An artificial intelligence-based constraint network system for concurrent engineering , 1992 .

[7]  Ronald E. Giachetti,et al.  A fuzzy constraint satisfaction system for design and manufacturing , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[8]  Allen C. Ward,et al.  Design compilers and the labeled interval calculus: a tutorial , 1992 .

[9]  Robert E. Young,et al.  SPARK: an artificial intelligence constraint network system for concurrent engineering , 1991 .

[10]  Rina Dechter,et al.  Network-Based Heuristics for Constraint-Satisfaction Problems , 1987, Artif. Intell..

[11]  E. Antonsson,et al.  Engineering design calculations with fuzzy parameters , 1992 .

[12]  E. K. Antonsson,et al.  Modeling imprecision in product design , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[13]  Arie Tzvieli Possibility theory: An approach to computerized processing of uncertainty , 1990, J. Am. Soc. Inf. Sci..

[14]  Durward K. Sobek,et al.  Set-based concurrent engineering and Toyota , 1994 .

[15]  E. Antonsson,et al.  The Method of Imprecision Compared to Utility Theory for Design Selection Problems , 1993 .

[16]  Robert E. Young,et al.  A system for design and concurrent engineering under imprecision , 1995, J. Intell. Manuf..

[17]  Robert E. Young,et al.  Implementation of a Logic-Based Support System for Concurrent Engineering , 1995, Data Knowl. Eng..

[18]  Robert E. Young,et al.  A constraint-system shell to support concurrent engineering approaches to design , 1994, Artif. Intell. Eng..