Prediction of Torque Characteristic on Barrier-Type SRM Using Stochastic Response Surface Methodology Combined With Moving Least Square

2004 ◽  
Vol 40 (2) ◽  
pp. 738-741 ◽  
Author(s):  
Y.-K. Kim ◽  
G.-H. Lee ◽  
J.-P. Hong ◽  
J. Hur ◽  
B.-K. Lee ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Least Square ◽  
Stochastic Response ◽  
Moving Least Square ◽  
Barrier Type

Metamodeling Development for Vehicle Frontal Impact Simulation

2001 ◽  
Author(s):  
R. J. Yang ◽  
N. Wang ◽  
C. H. Tho ◽  
J. P. Bobineau ◽  
B. P. Wang
Keyword(s):  
Response Surface ◽  
Impact Analysis ◽  
Hybrid Approach ◽  
Latin Hypercube Sampling ◽  
Element Model ◽  
Least Square ◽  
Frontal Impact ◽  
Response Surface Methods ◽  
Moving Least Square ◽  
Highly Nonlinear

Abstract Response surface methods or metamodels are commonly used to approximate large engineering systems. This paper presents a new metric for evaluating a response surface method or a metamodeling technique. Five response surface methods are studied: Stepwise Regression, Moving Least Square, Kriging, Multiquadratic, and Adaptive and Interactive Modeling System. A real world frontal impact design problem is used as an example, which is a complex, highly nonlinear, transient, dynamic, large deformation finite element model. The optimal Latin Hypercube Sampling method is used to distribute the sampling points uniformly over the entire design space. The Root Mean Square Error is used as the error indicator to study the accuracy and convergence rate of the metamodels for this vehicle impact analysis. A hybrid approach/strategy for selecting the best metamodels of impact responses is proposed.

Blank Optimization of Sheet Metal Forming Based on Response Surface Methodology

2011 ◽  
Vol 189-193 ◽  
pp. 2851-2855
Author(s):  
Zhi Guo An ◽  
Yu Zhang
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Sheet Metal ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Least Square ◽  
Thickness Variation ◽  
Optimization Strategy ◽  
Forming Process ◽  
Pareto Solution

The thickness variation of drawing part is usually very complicated, which causes the accurate calculation of the surface area of drawing part to be very difficult. Blank optimization of sheet metal forming is often considered as a multi-objective problem. A blank optimization strategy of sheet metal forming process was suggested based on Response Surface Methodology (RSM). Latin Hypercube Sampling (LHS) method was introduced to design the rational experimental samples; the objective function was defined based on crack factor and wrinkle factor; the accurate response surface for sheet metal forming problem was built by Least Square Method; Genetic Algorithm (GA) was adopted in optimization and Pareto solution was selected. The strategy was applied in blank optimization of an auto-part, this method was proved suitable for blank optimization of sheet metal forming.

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