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Kernel Density Estimation and Metropolis-Hastings Sampling in Process Capability Analysis of Unknown Distributions

[+] Author Affiliations
Wenzhen Huang, Ankit Pahwa

University of Massachusetts Dartmouth, North Dartmouth, MA

Zhenyu Kong

Oklahoma State University, Stillwater, OK

Paper No. MSEC2012-7299, pp. 651-660; 10 pages
  • ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing
  • ASME 2012 International Manufacturing Science and Engineering Conference
  • Notre Dame, Indiana, USA, June 4–8, 2012
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5499-0
  • Copyright © 2012 by ASME


Strong normality assumption is associated with widely used process capability indices such as cp, cpk. Violation of the assumption will mislead the interpretation in applications. A nonparametric method is proposed for density estimation of any unknown distribution. Kernels are used for density estimation and metropolis-hastings (M-H) algorithm is adopted to generate samples from the density. M-H sampling provides a tool to accommodate different kernel functions and flexibility of future extension to multivariate cases. Conformity (yield) based indices (yp, y) are adopted to replace cp, cpk. These indices can be conveniently assessed by the proposed kernel density based M-H algorithm (K-M-H). The method is validated by several simulation case studies.

Copyright © 2012 by ASME
Topics: Density , Cities



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