Paper Info
Title
Cycle Time Forecasting Models For Defect Inspection Process In Tft-Lcd Module Assembly
Abstract
Because most of the procedures in defect inspection process of TFT-LCD module assembly are examined manually through human vision, cycle time estimation for this particular process is complicated and usually deviated from actual observations considerably in practice. Hence, this study would like to apply the approaches of Bayesian network, linear discriminant analysis, and logistic regression to develop reliable prediction models for defect inspection cycle time. Potential explanatory variables like work-in-process, throughput, yield, and number of product mixes are considered for model construction. Applicability of these approaches is validated through an empirical study of TFT-LCD factory. From the perspective of prediction accuracy and flexibility, findings of this study suggest that logistic regression is a better choice for cycle time estimation than Bayesian network and discriminant analysis.
Year
Venue
Keywords
2008
ENGINEERING LETTERS
Cycle time prediction, Bayesian networks, Discriminant analysis, Logistic regression
Field
DocType
Volume
Computer science,Bayesian network,Liquid-crystal display,Artificial intelligence,Throughput,Linear discriminant analysis,Predictive modelling,Logistic regression,Empirical research,Machine learning
Journal
16
Issue
ISSN
Citations 
3
1816-093X
0
PageRank 
References 
Authors
0.34
1
1
Name
Order
Citations
PageRank
Chien-wen Shen1584.51