Multidisciplinary design optimisation of a recurve bow based on applications of the autogenetic design theory and distributed computing

被引:28
作者
Fritzsche, Matthias [1 ]
Kittel, Konstantin [1 ]
Blankenburg, Alexander [2 ]
Vajna, Sandor [1 ]
机构
[1] Univ Magdeburg, Fac Mech Engn, D-39106 Magdeburg, Germany
[2] NoaJa, D-39114 Magdeburg, Germany
关键词
product optimisation; autogenetic design theory; product development; distributed computing; genetic algorithms; process automation;
D O I
10.1080/17517575.2011.650216
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The focus of this paper is to present a method of multidisciplinary design optimisation based on the autogenetic design theory (ADT) that provides methods, which are partially implemented in the optimisation software described here. The main thesis of the ADT is that biological evolution and the process of developing products are mainly similar, i.e. procedures from biological evolution can be transferred into product development. In order to fulfil requirements and boundary conditions of any kind (that may change at any time), both biological evolution and product development look for appropriate solution possibilities in a certain area, and try to optimise those that are actually promising by varying parameters and combinations of these solutions. As the time necessary for multidisciplinary design optimisations is a critical aspect in product development, ways to distribute the optimisation process with the effective use of unused calculating capacity, can reduce the optimisation time drastically. Finally, a practical example shows how ADT methods and distributed optimising are applied to improve a product.
引用
收藏
页码:329 / 343
页数:15
相关论文
共 19 条
[1]  
Bercsey T., 1994, CAD CAM REPORT, V14, P98
[2]  
Bercsey T., 1994, CAD CAM REPORT, V13, P66
[3]  
Briggs J., 1990, ENTDECKUNG CHAOS
[4]  
Chen K.Z., 2003, P ICED 03 14 INT C E, P671
[5]  
Ehrlenspiel K., 2007, Integrierte Produktentwicklung - Denkablaufe, Methodeneinsatz, Zusammenarbeit
[6]   Comparison among five evolutionary-based optimization algorithms [J].
Elbeltagi, E ;
Hegazy, T ;
Grierson, D .
ADVANCED ENGINEERING INFORMATICS, 2005, 19 (01) :43-53
[7]  
Goldberg DE., 1989, GENETIC ALGORITHMS S, V13
[8]  
Haidn O., 2001, BASICS TRAINING SCI
[9]  
Jordan A., 2000, THESIS OTTOVONGUERIC
[10]  
Klein B., 2007, FEM GRUNDLAGEN ANWEN