Application of kernel principal component analysis to multi-characteristic parameter design problems

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The Taguchi method for robust parameter design traditionally deals with single characteristic parameter design problems. Extending the Taguchi method to the case of multi-characteristic parameter design (MCPD) problems requires an overall evaluation of multiple characteristics, for which the principal component analysis (PCA) has been frequently used. However, since the PCA is based on a linear transformation, it may not be effectively used for the data with complicated nonlinear structures. This paper develops a kernel PCA-based method that allows capturing nonlinear relationships among multiple characteristics in constructing a single aggregate performance measure. Applications of the proposed method to simulated and real experimental data show the advantages of the kernel PCA over the original PCA for solving MCPD problems.
Publisher
SPRINGER
Issue Date
2018-04
Language
English
Article Type
Article
Keywords

GREY RELATIONAL ANALYSIS; DISCHARGE MACHINING PROCESS; TAGUCHI METHOD; PERFORMANCE-CHARACTERISTICS; MULTIRESPONSE OPTIMIZATION; GENETIC ALGORITHM; ROBUST DESIGN; MANUFACTURING PROCESS; TURNING OPERATIONS; NEURAL-NETWORK

Citation

ANNALS OF OPERATIONS RESEARCH, v.263, no.1-2, pp.69 - 91

ISSN
0254-5330
DOI
10.1007/s10479-015-1889-2
URI
http://hdl.handle.net/10203/241305
Appears in Collection
IE-Journal Papers(저널논문)
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