A study on the optimization method for a multi-body system using the response surface analysis

An optimization method, which minimizes the characteristic value of a system using response surface analysis, is presented. Plackett-Burman design is used as a screening method. Using the response surface analysis, second order recursive model function is estimated as an objective function. To verif...

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Veröffentlicht in:Journal of mechanical science and technology Jg. 23; H. 4; S. 950 - 953
Hauptverfasser: Jung, Sung-Pil, Park, Tae-Won, Jun, Kab-Jin, Yoon, Ji-Won, Lee, Soo-Ho, Chung, Won-Sun
Format: Journal Article Tagungsbericht
Sprache:Englisch
Veröffentlicht: Heidelberg Korean Society of Mechanical Engineers 01.04.2009
Springer Nature B.V
대한기계학회
Schlagworte:
Applied sciences
Control
Design engineering
Design parameters
Dynamic models
Dynamical Systems
Engineering
Exact sciences and technology
Experimental design
Industrial and Production Engineering
Mathematical models
Mathematical programming
Mathematics
Mechanical Engineering
Operational research and scientific management
Operational research. Management science
Optimization
Probability and statistics
Recursive
Reliability theory. Replacement problems
Response surfaces
Sciences and techniques of general use
Statistics
Studies
Tables (data)
Vibration
기계공학
Plackett-Burman design
Sequential quadratic programming method
Central composite design
Design of experiments
Analysis of variance
F test
Process planning
N body system
Recursive function
Medical screening
Quadratic programming
Variance analysis
Modeling
Optimization
Screening
Sequential method
Experimental design
Response surface
Objective function
Dynamic programming
Reliability
Dynamic model
Factorial design
System analysis
ISSN:1738-494X, 1976-3824
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Beschreibung
Zusammenfassung:An optimization method, which minimizes the characteristic value of a system using response surface analysis, is presented. Plackett-Burman design is used as a screening method. Using the response surface analysis, second order recursive model function is estimated as an objective function. To verify the reliability of the model function, an F-test based on the analysis of variances table is used. Lastly, the sequential quadratic-programming method is used to find the value of design parameters. By applying the preceding procedure to a multi-body dynamic model, the optimization process presented in this study is verified.
Bibliographie:SourceType-Scholarly Journals-1
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G704-000058.2009.23.4.004
ISSN:1738-494X
1976-3824
DOI:10.1007/s12206-009-0319-2