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 (Formula presented.) 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 language | English (US) |
|---|---|
| Pages (from-to) | 219-242 |
| Number of pages | 24 |
| Journal | Journal of Quality Technology |
| Volume | 53 |
| Issue number | 3 |
| DOIs | |
| State | Published - 2021 |
Funding
This work was supported in part by National Science Foundation (NSF) Grant # CMMI-1265709 and Air Force Office of Scientific Research (AFOSR) Grant # FA9550-14-1-0032. Anh Tuan Bui also acknowledges support from the Vietnam Education Foundation. The authors thank the anonymous referees and editors for their careful reviews and helpful comments.
Keywords
- Anderson–Darling statistic
- Box–Pierce statistic
- Kullback–Leibler divergence
- anomaly detection
- generalized likelihood-ratio test (GLRT)
- image dissimilarity
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering
