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Research Papers: Design Theory and Methodology

Blind Kriging: A New Method for Developing Metamodels

[+] Author and Article Information
V. Roshan Joseph1

School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332roshan@isye.gatech.edu

Ying Hung

School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332

Agus Sudjianto

 Bank of America, Charlotte, NC 28255

1

Corresponding author.

J. Mech. Des 130(3), 031102 (Feb 04, 2008) (8 pages) doi:10.1115/1.2829873 History: Received June 28, 2006; Revised June 14, 2007; Published February 04, 2008

Kriging is a useful method for developing metamodels for product design optimization. The most popular kriging method, known as ordinary kriging, uses a constant mean in the model. In this article, a modified kriging method is proposed, which has an unknown mean model. Therefore, it is called blind kriging. The unknown mean model is identified from experimental data using a Bayesian variable selection technique. Many examples are presented, which show remarkable improvement in prediction using blind kriging over ordinary kriging. Moreover, a blind kriging predictor is easier to interpret and seems to be more robust against mis-specification in the correlation parameters.

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Copyright © 2008 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Finite element model of engine head and block joint sealing assembly

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Figure 2

Half-normal plot of ∣β̂i∣’s at m=0

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Figure 3

Plots of CVPE(m) and R2(m) in Example 1

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Figure 4

Plots of CVPE(m) and R2(m) in Example 2

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Figure 5

Density plot for the prediction errors in Example 2

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Figure 6

Density plot for the prediction errors in Example 3

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Figure 7

RMSPE values for different θ in Example 3

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