Robust monitoring of stochastic textured surfaces

Anh Tuan Bui, Daniel W. Apley*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Stochastic textured surfaces (STSs) do not have well-defined features, and their quality characteristics are reflected through the stochastic nature of their surface textures. Monitoring general global changes in the stochastic nature of STSs is a relatively new, yet important problem. The limited literature for solving this problem has not considered the common situation in which the normal, in-control STS data are subject to structured surface-to-surface variation in their stochastic nature, due to the challenging nature of this problem. In this paper, we propose a dissimilarity-based multivariate control charting approach for monitoring general global changes in STSs in the presence of such structured in-control variation. Our approach is novel in that it quantifies the level of abnormality from multiple ‘spanning points’, instead of a single reference as in prior work. The spanning points are selected via dissimilarity-based manifold learning and space filling sampling methods. We test our approach with simulated and real textile examples and demonstrate its superior robustness to the structured in-control variation. Our approach has potential to provide a general control charting framework for any applications involving complex data structures other than STS data.

Original languageEnglish (US)
JournalInternational Journal of Production Research
DOIs
StateAccepted/In press - 2021

Keywords

  • Dissimilarity
  • outlier detection
  • Phase I analysis
  • statistical process control (SPC)
  • variation

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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