DocumentCode :
510164
Title :
Drawing Process Design Based on Fuzzy Knowledge Reasoning
Author :
Gao Jun ; Ji Tingwei ; Zhao Guoqun
Author_Institution :
Sch. of Mech. andElectrical Eng., Shandong Univ. at Weihai, Weihai, China
Volume :
2
fYear :
2009
fDate :
7-8 Nov. 2009
Firstpage :
73
Lastpage :
77
Abstract :
As well known, stamping process design involves lots of empirical knowledge, which prolongs the cycle of design and manufacturing and limits the competitiveness of the enterprises. With the development of computer technologies, CAD/CAM/CAE technologies have been widely applied to the design of the stamping processes and dies, but it still needs a large number of engineers´ experiences. In this paper, the process of traditional stamping process design using fuzzy theory was presented, and a drawing process design system was established, which mainly includes input and output module, knowledge data-base module, process design module. And then, the intelligent adjustment model of design process that provided by fuzzy theory converting knowledge data-base to non-linear mapping was established. Finally, a typical part was taken as an example to demonstrate the running process of the system; the result showed that the system is effective.
Keywords :
CAD/CAM; computer aided engineering; drawing (mechanical); fuzzy set theory; inference mechanisms; knowledge based systems; mechanical engineering computing; metal stamping; process design; production engineering computing; CAD; CAE technology; CAM; drawing process design; fuzzy knowledge reasoning; fuzzy theory; intelligent adjustment model; knowledge data-base module; nonlinear mapping; stamping process design; CADCAM; Computer aided engineering; Computer aided manufacturing; Design automation; Design engineering; Engineering drawings; Fuzzy reasoning; Fuzzy systems; Manufacturing processes; Process design; drawing process design; fuzzy knowledge reasoning; knowledge data-base;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
Type :
conf
DOI :
10.1109/AICI.2009.367
Filename :
5376387
Link To Document :
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