A Monitoring and Diagnostic Approach for Stochastic Textured Surfaces

Anh Tuan Bui, Daniel W. Apley*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


We develop a supervised-learning-based approach for monitoring and diagnosing texture-related defects in manufactured products characterized by stochastic textured surfaces that satisfy the locality and stationarity properties of Markov random fields. Examples of stochastic textured surface data include images of woven textiles; image or surface metrology data for machined, cast, or formed metal parts; microscopy images of material microstructure samples; etc. To characterize the complex spatial statistical dependencies of in-control samples of the stochastic textured surface, we use rather generic supervised learning methods, which provide an implicit characterization of the joint distribution of the surface texture. We propose two spatial moving statistics, which are computed from residual errors of the fitted supervised learning model, for monitoring and diagnosing local aberrations in the general spatial statistical behavior of newly manufactured stochastic textured surface samples in a statistical process control context. We illustrate the approach using images of textile fabric samples and simulated two-dimensional stochastic processes, for which the algorithm successfully detects local defects of various natures. Supplemental discussions, results, data and computer codes are available online.

Original languageEnglish (US)
Pages (from-to)1-13
Number of pages13
Issue number1
StatePublished - Jan 2 2018


  • Anderson–Darling statistic
  • Box–Pierce statistic
  • Defect detection
  • Markov random field
  • Statistical process control (SPC)
  • Supervised learning

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics


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