Modeling multivariate profiles using Gaussian process-controlled B-splines

Mithun Ghosh, Yongxiang Li, Li Zeng, Zijun Zhang, Qiang Zhou

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Due to the increasing presence of profile data in manufacturing, profile monitoring has become one of the most popular research directions in statistical process control. The core of profile monitoring is how to model the profile data. Most of the current methods deal with univariate profile modeling where only within-profile correlation is considered. In this article, a linear mixed-effect model framework is adopted for dealing with multivariate profiles, having both within- and between-profile correlations. For better flexibility yet reduced computational cost, we propose to construct the random component of the linear mixed effects model using B-splines, whose control points are governed by a multivariate Gaussian process. Extensive simulations have been conducted to compare the model with classic models. In the case study, the proposed model is applied to the transmittance profiles from the low-emittance glasses.

Original languageEnglish (US)
Pages (from-to)787-798
Number of pages12
JournalIISE Transactions
Volume53
Issue number7
DOIs
StatePublished - 2021

Keywords

  • B-spline
  • Linear mixed effects model
  • multivariate Gaussian process
  • profile monitoring

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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