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Research Papers

High Dimensional Model Representation With Principal Component Analysis

[+] Author and Article Information
Kambiz Haji Hajikolaei

e-mail: khajihaj@sfu.ca

G. Gary Wang

Professor
e-mail: gary_wang@sfu.ca
Product Design and Optimization
Laboratory (PDOL),
School of Mechatronic Systems Engineering,
Simon Fraser University,
250-13450 102 Avenue,
Surrey, BC V3T0A3, Canada

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 13, 2013; final manuscript received September 6, 2013; published online October 17, 2013. Assoc. Editor: Michael Kokkolaras.

J. Mech. Des 136(1), 011003 (Oct 17, 2013) (11 pages) Paper No: MD-13-1120; doi: 10.1115/1.4025491 History: Received March 13, 2013; Revised September 06, 2013

In engineering design, spending excessive amount of time on physical experiments or expensive simulations makes the design costly and lengthy. This issue exacerbates when the design problem has a large number of inputs, or of high dimension. High dimensional model representation (HDMR) is one powerful method in approximating high dimensional, expensive, black-box (HEB) problems. One existing HDMR implementation, random sampling HDMR (RS-HDMR), can build an HDMR model from random sample points with a linear combination of basis functions. The most critical issue in RS-HDMR is that calculating the coefficients for the basis functions includes integrals that are approximated by Monte Carlo summations, which are error prone with limited samples and especially with nonuniform sampling. In this paper, a new approach based on principal component analysis (PCA), called PCA-HDMR, is proposed for finding the coefficients that provide the best linear combination of the bases with minimum error and without using any integral. Several benchmark problems of different dimensionalities and one engineering problem are modeled using the method and the results are compared with RS-HDMR results. In all problems with both uniform and nonuniform sampling, PCA-HDMR built more accurate models than RS-HDMR for a given set of sample points.

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Figures

Grahic Jump Location
Fig. 1

The geometric representation of PCA

Grahic Jump Location
Fig. 2

R-square values of the first ten benchmark functions (average of 20 runs, sampling type 1)

Grahic Jump Location
Fig. 3

RAAE values of the first ten benchmark functions (average of 20 runs, sampling type 1)

Grahic Jump Location
Fig. 4

RMAE values of the first ten benchmark functions (average of 20 runs, sampling type 1)

Grahic Jump Location
Fig. 5

RAAE values of the first ten benchmark functions (average of 20 runs, sampling type 2)

Grahic Jump Location
Fig. 6

RMAE values of the first ten benchmark functions (average of 20 runs, sampling type 2)

Grahic Jump Location
Fig. 7

Sine function (solid), normal PCA-HDMR approximation (dash-dotted), PCA-HDMR with weight 10 after x≥0 (dashed), and PCA-HDMR with weight 1000 after x≥0 (dotted)

Grahic Jump Location
Fig. 8

PCA-HDMR results using different number of components

Grahic Jump Location
Fig. 9

Three-part assembly problem and the related fixtures [33]

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