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

被引：16

作者：

Zeng, Li ^{[1
]}

Chen, Nan ^{[2
]}

机构：

[1] Univ Texas Arlington, Dept Ind & Mfg Syst Engn, Arlington, TX 76019 USA

[2] Natl Univ Singapore, Dept Ind & Syst Engn, Singapore 117576, Singapore

来源：

IIE TRANSACTIONS | 2015年 / 47卷 / 02期

基金：

中国国家自然科学基金; 美国国家科学基金会;

关键词：

hierarchical linear mixed-effect (HLME) model; Phase I analysis; Bayes factors; polynomial models; Gibbs sampling; VARIANCE COMPONENT MODELS; MONTE-CARLO METHODS; POLYNOMIAL PROFILES; MARGINAL LIKELIHOOD; LINEAR PROFILES; MIXED MODELS; OUTPUT; CHOICE;

D O I：

10.1080/0740817X.2014.892230

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：109 / 124

页数：16