An exploratory analysis approach for understanding variation in stochastic textured surfaces

Anh Tuan Bui, Daniel Apley*

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Stochastic textured surface data are increasingly common in industrial quality control and other settings. Although there are a number of recently developed methods for understanding variation (e.g., due to manufacturing inconsistency) across a set of profiles or other multivariate quality control data, these existing methods are not applicable to stochastic textured surfaces due to their stochastic nature. One challenge is that it is not straightforward how to define distances or dissimilarities between surface samples. An approach is developed for understanding variation in stochastic textured surfaces by deriving a new pairwise dissimilarity measure between surface samples and using these dissimilarities within a manifold learning framework to discover a low-dimensional parameterization of the surface variation patterns. Visualizing how the stochastic nature of the surfaces changes as the manifold parameters are varied helps build an understanding of the individual physical characteristic of each variation pattern. The approach is demonstrated with simulation and textile examples, in which the physical characteristics of the variation patterns are clearly revealed. The computer codes for implementing the approach are available in the spc4sts R package.

Original languageEnglish (US)
Pages (from-to)33-50
Number of pages18
JournalComputational Statistics and Data Analysis
Volume137
DOIs
StatePublished - Sep 1 2019

Fingerprint

Exploratory Analysis
Dissimilarity
Quality Control
Quality control
Dissimilarity Measure
Manifold Learning
Parameterization
Inconsistency
Pairwise
Textiles
Manufacturing

Keywords

  • Dimension reduction
  • Kullback–Leibler divergence
  • Manifold learning
  • Multidimensional scaling
  • Phase I analysis
  • Statistical process control

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

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title = "An exploratory analysis approach for understanding variation in stochastic textured surfaces",
abstract = "Stochastic textured surface data are increasingly common in industrial quality control and other settings. Although there are a number of recently developed methods for understanding variation (e.g., due to manufacturing inconsistency) across a set of profiles or other multivariate quality control data, these existing methods are not applicable to stochastic textured surfaces due to their stochastic nature. One challenge is that it is not straightforward how to define distances or dissimilarities between surface samples. An approach is developed for understanding variation in stochastic textured surfaces by deriving a new pairwise dissimilarity measure between surface samples and using these dissimilarities within a manifold learning framework to discover a low-dimensional parameterization of the surface variation patterns. Visualizing how the stochastic nature of the surfaces changes as the manifold parameters are varied helps build an understanding of the individual physical characteristic of each variation pattern. The approach is demonstrated with simulation and textile examples, in which the physical characteristics of the variation patterns are clearly revealed. The computer codes for implementing the approach are available in the spc4sts R package.",
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An exploratory analysis approach for understanding variation in stochastic textured surfaces. / Bui, Anh Tuan; Apley, Daniel.

In: Computational Statistics and Data Analysis, Vol. 137, 01.09.2019, p. 33-50.

Research output: Contribution to journalArticle

TY - JOUR

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AU - Apley, Daniel

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N2 - Stochastic textured surface data are increasingly common in industrial quality control and other settings. Although there are a number of recently developed methods for understanding variation (e.g., due to manufacturing inconsistency) across a set of profiles or other multivariate quality control data, these existing methods are not applicable to stochastic textured surfaces due to their stochastic nature. One challenge is that it is not straightforward how to define distances or dissimilarities between surface samples. An approach is developed for understanding variation in stochastic textured surfaces by deriving a new pairwise dissimilarity measure between surface samples and using these dissimilarities within a manifold learning framework to discover a low-dimensional parameterization of the surface variation patterns. Visualizing how the stochastic nature of the surfaces changes as the manifold parameters are varied helps build an understanding of the individual physical characteristic of each variation pattern. The approach is demonstrated with simulation and textile examples, in which the physical characteristics of the variation patterns are clearly revealed. The computer codes for implementing the approach are available in the spc4sts R package.

AB - Stochastic textured surface data are increasingly common in industrial quality control and other settings. Although there are a number of recently developed methods for understanding variation (e.g., due to manufacturing inconsistency) across a set of profiles or other multivariate quality control data, these existing methods are not applicable to stochastic textured surfaces due to their stochastic nature. One challenge is that it is not straightforward how to define distances or dissimilarities between surface samples. An approach is developed for understanding variation in stochastic textured surfaces by deriving a new pairwise dissimilarity measure between surface samples and using these dissimilarities within a manifold learning framework to discover a low-dimensional parameterization of the surface variation patterns. Visualizing how the stochastic nature of the surfaces changes as the manifold parameters are varied helps build an understanding of the individual physical characteristic of each variation pattern. The approach is demonstrated with simulation and textile examples, in which the physical characteristics of the variation patterns are clearly revealed. The computer codes for implementing the approach are available in the spc4sts R package.

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KW - Multidimensional scaling

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