Bayesian hierarchical modeling for monitoring optical profiles in low-E glass manufacturing processes

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

14 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)109-124
Journal / PublicationIIE Transactions (Institute of Industrial Engineers)
Volume47
Issue number2
Publication statusPublished - 1 Feb 2015
Externally publishedYes

Abstract

Low-emittance (low-E) glass manufacturing has become an important sector of the glass industry for energy efficiency of such glasses. However, the quality control scheme in the current processes is rather primitive and advanced statistical quality control methods need to be developed. As the first attempt for this purpose, this article considers monitoring of optical profiles, which are typical quality measurements in low-E glass manufacturing. A Bayesian hierarchical approach is proposed for modeling the optical profiles, which conducts model selection and estimation in an integrated framework. The effectiveness of the proposed approach is validated in a numerical study, and its use in Phase I analysis of optical profiles is demonstrated in a case study. The proposed approach will lay a foundation for quality control and variation reduction in low-E glass manufacturing.

Research Area(s)

  • Bayes factors, Gibbs sampling, hierarchical linear mixed-effect (HLME) model, Phase I analysis, polynomial models

Bibliographic Note

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