scholarly journals Use of Response Surface Metamodels for Identification of Stiffness and Damping Coefficients in a Simple Dynamic System

2005 ◽  
Vol 12 (5) ◽  
pp. 317-331 ◽  
Author(s):  
A.C. Rutherford ◽  
D.J. Inman ◽  
G. Park ◽  
F.M. Hemez
Keyword(s):  
Parameter Identification ◽  
Response Surface ◽  
Structural Dynamics ◽  
Design Space ◽  
Ease Of Use ◽  
Surface Model ◽  
Training Requirements ◽  
System Parameters ◽  
Polynomial Response Surface ◽  
Stiffness And Damping

Metamodels have been used with success in many areas of engineering for decades but only recently in the field of structural dynamics. A metamodel is a fast running surrogate that is typically used to aid an analyst or test engineer in the fast and efficient exploration of the design space. Response surface metamodels are used in this work to perform parameter identification of a simple five degree of freedom system, motivated by their low training requirements and ease of use. In structural dynamics applications, response surface metamodels have been utilized in a forward sense, for activities such as sensitivity analysis or uncertainty quantification. In this study a polynomial response surface model is developed, relating system parameters to measurable output features. Once this relationship is established, the response surface is used in an inverse sense to identify system parameters from measured output features.A design of experiments is utilized to choose points, representing a fraction of the full design space of interest, for fitting the response surface metamodel. Two parameters commonly used to characterize damage in a structural system, stiffness and damping, are identified. First changes are identified and located with success in a linear 5DOF system. Then parameter identification is attempted with a nonlinear 5DOF system and limited success is achieved. This work will demonstrate that use of response surface metamodels in an inverse sense shows promise for use in system parameter identification for both linear and weakly nonlinear systems and that the method has potential for use in damage identification applications.

scholarly journals Novel Two-Stage Method for Low-Order Polynomial Model

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Cheng Yan ◽  
Xiuli Shen ◽  
Fushui Guo
Keyword(s):  
Response Surface ◽  
Surface Model ◽  
Two Stage ◽  
Nonlinear Simulation ◽  
Weakly Nonlinear ◽  
Second Stage ◽  
Sampling Points ◽  
Order Polynomial ◽  
Polynomial Response Surface ◽  
Low Order

One of the most popular statistical models is a low-order polynomial response surface model, i.e., a polynomial of first order or second order. These polynomials can be used for global metamodels in weakly nonlinear simulation to approximate their global tendency and local metamodels in response surface methodology (RSM), which has been studied in various applications in engineering design and analysis. The order of the selected polynomial determines the number of sampling points (input combinations) and the resulting accuracy (validity, adequacy). This paper derives a novel method to obtain an accurate high-order polynomial while requiring fewer sampling points. This method uses a two-stage procedure such that the second stage modifies the low-order polynomial estimated in the first stage; this second stage does not require new points. This paper evaluates the performance of the method numerically by using several test functions. These numerical results show that the proposed method can provide more accurate predictions than the traditional method.

Fixture Configuration Design for Sheet Metal Laser Welding With a Two-Stage Response Surface Methodology

2001 ◽  
Author(s):  
Bing Li ◽  
Boon-wai Shiu ◽  
Kwok-jing Lau
Keyword(s):  
Response Surface Methodology ◽  
Laser Welding ◽  
Response Surface ◽  
Sheet Metal ◽  
Robust Design ◽  
Design Space ◽  
Surface Model ◽  
Configuration Design ◽  
Two Stage ◽  
Fixture Configuration

Abstract In this paper a two-stage response surface methodology is developed for the robust fixture configuration design of sheet metal laser welding. The first stage is to optimally determine the Robust Design Space (RDS) where the relatively less-sensitive design over the entire feasible design space can be obtained. A weighted objective function combining both robustness and performance are taken in this stage. The degree of metal fit-up is taken as performance characteristic. Within RDS a second-order response surface model is fitted by a 3k fractional factorial design in the second stage. Thus, based on the new methodology the robust design results under the new fixturing scheme can be obtained. Illustrative example shows that the presented method can lead to a robust fixturing scheme and the influential design locators can be detected.

Response Surface Algorithms for Engine Mount Optimization in Motorcycles

2009 ◽  
Author(s):  
Sudhir Kaul ◽  
Anoop K. Dhingra
Keyword(s):  
Response Surface ◽  
Optimization Problem ◽  
Vibration Isolation ◽  
Design Space ◽  
Substantial Reduction ◽  
Response Surface Model ◽  
Fractional Factorial ◽  
Surface Model ◽  
Design Variables ◽  
Engine Mount

This paper presents a Response Surface Modeling (RSM) approach for solving the engine mount optimization problem for a motorcycle application. A theoretical model that captures the structural dynamics of a motorcycle engine mount system is first used to build the response surface model. The response surface model is then used to solve the engine mount optimization problem for enhanced vibration isolation. Design of Experiments (DOE), full factorial and fractional factorial formulations, are used to construct the governing experiments. Normal probability plots are used to determine the statistical significance of the variables and the significant variables are then used to build the response surface. The design variables for the engine mount optimization problem include mount stiffness, position vectors and orientation vectors. It is seen that RSM leads to a substantial reduction in computational effort and yields a simplified input-output relationship between the variables of interest. However, as the number of design variables increases and as the response becomes irregular, conventional use of RSM is not viable. Two algorithms are proposed in this paper to overcome the issues associated with the size of the governing experiments and problems associated with modeling of the orientation variables. The proposed algorithms divide the design space into sub-regions in order to manage the size of the governing experiments without significant confounding of variables. An iterative procedure is used to overcome high response irregularity in the design space, particularly due to orientation variables.

Numerical Study on the Flash Gutter of Forging Die Using Response Surface Methodology

2010 ◽  
Vol 156-157 ◽  
pp. 1352-1355
Author(s):  
Yu Zhang ◽  
Zhi Guo An ◽  
Wei Li
Keyword(s):  
Genetic Algorithms ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Numerical Study ◽  
Design Method ◽  
Response Surface Model ◽  
Surface Model ◽  
Polynomial Response Surface ◽  
Forging Die ◽  
Better Than

Aiming at the lack of the traditional design method of the forging die, the response surface methodology based on design of experiments is proposed to optimize the parameters of flash gutter in forging die. According to the central composite inscribed experiment design, numerical simulation was carried out. On this basis, the quadratic polynomial response surface model was built by regressive analysis. The optimized flash gutter sizes are obtained by analyzing the response surface model with an objective of minimizing flash volume. The forging die of automobile flange has been studied as an illustration to validate the application of the response surface methodology. Finally, the comparison was done using genetic algorithms and the results show that the response surface methodology is better than the genetic algorithms with shorten time and better effect when the factor is small.

Sign in / Sign up

Export Citation Format

Share Document