Multimode Geometric-Profile Monitoring with Correlated Image Data and Its Application to Nanoparticle Self-Assembly Processes

被引:15
作者
Park, Chiwoo [1 ]
Shrivastava, Abhishek K. [1 ]
机构
[1] Florida State Univ, Dept Ind & Mfg Engn, Tallahassee, FL 32306 USA
关键词
Autocorrelated Process; Bayes Factor; Functional Data; Growth Processes; Mixture of Time Series; Nanomanufacturing; STATISTICAL PROCESS-CONTROL; GAUSSIAN MIXTURE MODEL; CONTROL CHARTS; NONPARAMETRIC ALTERNATIVES; LINEAR PROFILES; MEDICAL STATISTICS; BAYESIAN-INFERENCE; LIKELIHOOD; SHAPE;
D O I
10.1080/00224065.2014.11917966
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
We propose a new method for monitoring changes in geometric profiles of objects, where the geometric profiles may change through various modes in a dynamic process. This work is motivated by the need for monitoring changes in geometric shape and sizes of nanoparticles during their chemical self-assembly process; the changes often occur through many different modes before converging to the final state. The proposed multimode geometric-profile monitoring method addresses three issues specific to this process, all of which have never been addressed together by existing process-monitoring methods profiling of functional data, monitoring of multimode processes, and monitoring of time-correlated processes. The new profile-monitoring method consists of two phases. In phase I, we characterize multiple modes of geometric shape changes under in-control process conditions given a sequence of geometric observations on products. We propose using a mixture of time-series models for this characterization and present an exact Gibbs sampling procedure for Bayesian estimation of model parameters. In phase II, we test whether a new observation of product geometries sampled at a certain time in the current process run exhibits significant out-of-control symptoms. We propose a Bayes factor score-based criteria for this testing. The proposed method is empirically verified using simulated datasets and a real dataset from a nanoparticle self-assembly process.
引用
收藏
页码:216 / 233
页数:18
相关论文
共 59 条
[1]   TIME-SERIES MODELING FOR STATISTICAL PROCESS-CONTROL [J].
ALWAN, LC ;
ROBERTS, HV .
JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 1988, 6 (01) :87-95
[2]   NONPARAMETRIC QUALITY-CONTROL CHARTS BASED ON THE SIGN STATISTIC [J].
AMIN, RW ;
REYNOLDS, MR ;
BAKIR, S .
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 1995, 24 (06) :1597-1623
[3]  
[Anonymous], 1997, THESIS U OXFORD
[4]  
Apley DW, 1999, IIE TRANS, V31, P1123, DOI 10.1080/07408179908969913
[5]   Marginal likelihood and Bayes factors for Dirichlet process mixture models [J].
Basu, S ;
Chib, S .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2003, 98 (461) :224-235
[6]   Bayesian and conditional frequentist testing of a parametric model versus nonparametric alternatives [J].
Berger, JO ;
Guglielmi, A .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2001, 96 (453) :174-184
[7]  
Boor C.D., 2001, A Practical Guide to Splines
[8]   Nonparametric Multivariate Control Charts Based on a Linkage Ranking Algorithm [J].
Bush, Helen Meyers ;
Chongfuangprinya, Panitarn ;
Chen, Victoria C. P. ;
Sukchotrat, Thuntee ;
Kim, Seoung Bum .
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 2010, 26 (07) :663-675
[9]   Plasmon-assisted local temperature control to pattern individual semiconductor nanowires and carbon nanotubes [J].
Cao, Linyou ;
Barsic, David N. ;
Guichard, Alex R. ;
Brongersma, Mark L. .
NANO LETTERS, 2007, 7 (11) :3523-3527
[10]   Practical Design of Generalized Likelihood Ratio Control Charts for Autocorrelated Data [J].
Capizzi, Giovanna ;
Masarotto, Guido .
TECHNOMETRICS, 2008, 50 (03) :357-370