spc4sts: Statistical process control for stochastic textured surfaces in R

Anh Tuan Bui, Daniel W. Apley*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Stochastic textured surface (STS) data (e.g., material microstructure microscopy images) are increasingly common in many quality control settings. Because of their stochastic nature, performing statistical process control (SPC) for STS data without requiring advanced knowledge of abnormal behavior is challenging, and there is no existing SPC software available to solve this problem. This article introduces the spc4sts R package, which is the first implementation of recent developments that address SPC problems for STS data. The package provides tools for modeling, monitoring for defects and changes, and diagnosing variation and other patterns or modes that occur due to manufacturing and processing conditions.

Original languageEnglish (US)
JournalJournal of Quality Technology
DOIs
StateAccepted/In press - Jan 1 2020

Keywords

  • Anderson–Darling statistic
  • anomaly detection
  • Box–Pierce statistic
  • generalized likelihood-ratio test (GLRT)
  • image dissimilarity
  • Kullback–Leibler divergence

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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