Paper Info
Title
Determining manufacturing parameters to suppress system variance using linear and non-linear models
Abstract
Determining manufacturing parameters for a new product is fundamentally a difficult problem, because there has little suggestion information. There are several researches on this topic, and most of them focus on single specific model or the engineer's experience. As to other approaches, the usage of multiple models may be an alternative approach to help determining the parameters. This research proposed an aggregation of multiple regression and back-propagation neural network to find the manufacturing parameter's limits (upper and lower limits). A real-problem of a new product parameter setting model in the real Thin Film Transistor-Liquid Crystal Display (TFT-LCD) manufacturing company is demonstrated, where three forecasting models are applied, and t test is used to judge which models are the suitable ones. Finally, we average the computed parameter values from the chosen models to suppress the system variance. The empirical results show that the proposed method is successful in suppressing the system variance and improving the production yields.
Year
DOI
Venue
2012
10.1016/j.eswa.2011.09.067
Expert Syst. Appl.
Keywords
Field
DocType
manufacturing parameter,new product parameter,system variance,multiple model,chosen model,new product,multiple regression,forecasting model,computed parameter value,non-linear model,manufacturing
Data mining,Manufacturing,Computer science,Non linear model,Crystal display,Artificial neural network,Multiple Models,Linear regression,New product development
Journal
Volume
Issue
ISSN
39
4
0957-4174
Citations 
PageRank 
References 
0
0.34
10
Authors
5
Name
Order
Citations
PageRank
Der-chiang Li137431.57
Wen-Chih Chen2151.45
Chiao-Wen Liu31428.56
Che-Jung Chang4365.62
Chien-Chih Chen511120.42