Spc4sts: Statistical process control for stochastic textured surfaces in R. Issue 3 (27th May 2021)

• Record Type: Journal Article
• Authors: Bui, Anh Tuan; Apley, Daniel W.
• Is Part Of: Journal of quality technology. Volume 53:Issue 3(2021)
• Page Start: 219
• Page End: 242
• Publication Date: 2021-05-27
• Subjects: Anderson–Darling statistic -- anomaly detection -- Box–Pierce statistic -- generalized likelihood-ratio test (GLRT) -- image dissimilarity -- Kullback–Leibler divergence; Quality control -- Periodicals; Qualité -- Contrôle -- Périodiques; Quality control; Quality control; Periodicals; 620.0045
• DOI: 10.1080/00224065.2019.1707730
• Languages: English
• ISSNs: 0022-4065

Abstract

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.