A Monitoring and Diagnostic Approach for Stochastic Textured Surfaces

ABSTRACT

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.

KEYWORDS:

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

Supplementary Materials

In the supplementary material, we discuss the types of defects that our algorithm can detect. We also show diagnostic images for the simulation example when w = 15 and 25 and for the textile example using our approach with w = 15, the EPWMV approach with w = 25 and the algorithm in Lin (2007a). The textile image data and R codes are also included in the Supplementary

Acknowledgments

This work was supported in part by NSF Grant # CMMI-1265709 and AFOSR Grant # FA9550-14-1-0032, which the authors gratefully acknowledge. Anh Tuan Bui also acknowledges support from the Vietnam Education Foundation. The authors thank the Editor and the anonymous Associate Editor and Referees for helping to improve the article.