Title :
Modeling of generalized dynamic constraints network based on simulation and expand approximate method (EAM)
Author :
Wang, Wei-Ming ; Hu, Jie ; Li, Da-Yong ; Peng, Ying-hong ; Zhang, Xin
Author_Institution :
Sch. of Mech. Eng., Shanghai Jiao Tong Univ., China
Abstract :
This research work aims to develop a GDCN (generalized dynamic constraint network) that enables designers to consider at the early stages of the design process all activities associated with product´s life cycle. This research article discusses the development of effective modeling method for domain level constraints using a combination of both numerical simulation and approximate techniques. Firstly, the definition and mathematical model of GDCN are given. Secondly, the expand approximate method (EAM) is introduced, in which numerical simulation and adapting response surface methodology (ARSM) are integrated. Thirdly, the effective modeling method of domain level constraints is constructed through abstracting relations between the input and the output (domain performance index) from the simulation data based on EAM. Finally, the design of crank and connecting rod in the V6 engine as example is given to show the validity of the modeling method.
Keywords :
approximation theory; concurrent engineering; constraint theory; numerical analysis; product design; product life cycle management; production engineering computing; V6 engine; abstracting relation; adapting response surface methodology; approximate technique; connecting rod; crank design; domain level constraint; domain performance index; expand approximate method; generalized dynamic constraints network; numerical simulation; product life cycle; Concurrent engineering; Differential equations; Engines; Fuzzy logic; Joining processes; Mathematical model; Mechanical engineering; Numerical simulation; Process design; Response surface methodology; Domain level constraints; Expand Approximate Method (EAM); Generalized Dynamic Constraint Network (GDCN); Numerical simulation;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527415