Abstract
Stochastic textured surfaces (STSs) do not have well-defined features, and their quality characteristics are reflected through the stochastic nature of their surface textures. Monitoring general global changes in the stochastic nature of STSs is a relatively new, yet important problem. The limited literature for solving this problem has not considered the common situation in which the normal, in-control STS data are subject to structured surface-to-surface variation in their stochastic nature, due to the challenging nature of this problem. In this paper, we propose a dissimilarity-based multivariate control charting approach for monitoring general global changes in STSs in the presence of such structured in-control variation. Our approach is novel in that it quantifies the level of abnormality from multiple ‘spanning points’, instead of a single reference as in prior work. The spanning points are selected via dissimilarity-based manifold learning and space filling sampling methods. We test our approach with simulated and real textile examples and demonstrate its superior robustness to the structured in-control variation. Our approach has potential to provide a general control charting framework for any applications involving complex data structures other than STS data.
Original language | English (US) |
---|---|
Pages (from-to) | 5071-5086 |
Number of pages | 16 |
Journal | International Journal of Production Research |
Volume | 60 |
Issue number | 16 |
DOIs | |
State | Published - 2022 |
Keywords
- Dissimilarity
- Phase I analysis
- outlier detection
- statistical process control (SPC)
- variation
ASJC Scopus subject areas
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering