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Isogrid Structure Of The Mechanical Properties Of Research And Optimal Design

Posted on:2005-07-19Degree:MasterType:Thesis
Country:ChinaCandidate:Z Y ShiFull Text:PDF
GTID:2192360125953825Subject:Mechanical Manufacturing and Automation
Abstract/Summary:PDF Full Text Request
In the context of the research of application technology of ISOGRID structure to artillery structures, this thesis mainly researches its mechanical characteristics and the methodology of optimal design. The main research work is composed of followings:1) Technology of parameterized characterization of geometry and structural characteristics of ISOGRID structure is researched with finite element technique and the parameterized modeling facilities provided by universal CAE software. Characteristics of its stress and deformation in different load cases is obtained, and the relations between structural parameters of ISOGRID structure and its stiffness, strength, stability, dynamic characteristics is probed.2) A method of intelligent global optimal design of ISOGRID structures accounting for multiple objectives, multi-level parameters and grid layouts is put forward by integrating Artificial Neural Networks, Genetic Algorithm and parameterized modeling technique.3) Through the programming environments of secondary development in the integrated CAE software platform of MSC.NASTRAN and MSC.PATRAN, the combination of parameterized modeling, finite element analysis and the algorithm of intelligent optimization is realized. The menu interface of parameter debugging and optimal design is also developed.4) Mechanical characteristics of sample ISOGRID trunk under the actual loading conditions of a howitzer trail is researched, its optimal design is then successfully accomplished with the method and software in this thesis.5) A prototype of optimized ISOGRID steel panel is manufactured, its dynamic characteristics experiment is successfully carried out.
Keywords/Search Tags:ISOGRID, Finite Element Analysis, Structural Optimization, Intelligent Optimization, Neural Networks, Genetic Algorithm, Parameterized Modeling
PDF Full Text Request
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