A multi-model approach to determine early manufacturing parameters for small-data-set prediction

Der-Chiang Li, Chiao Wen Liu, Wen Chih Chen

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Constructing an accurate prediction model from a small training data set is an important but difficult task in the field of forecasting. This is because when the data size is small, the incomplete data may mean that the model produced cannot sufficiently represent the true data structure or cause the model training to be overfitted. To address this issue, this paper presents an approach that combines multiple prediction models to extract data information in multiple facets. In the multi-model approach, a compromise weight method is proposed to determine the relative reliability of each of the prediction model. The methods used include multiple regression, artificial neural network, and support vector machines for regression. A thin-film transistor liquid crystal display manufacturing case study is used to illustrate the details of this research. The empirical results not only show that the proposed multi-model can reduce the manufacturing variation and increase the production yield, but also can propose a robust and reliable parameter interval to the online engineers in the early manufacturing stage.

Original languageEnglish
Pages (from-to)6679-6690
Number of pages12
JournalInternational Journal of Production Research
Volume50
Issue number23
DOIs
Publication statusPublished - 2012 Dec 1

Fingerprint

Thin film transistors
Prediction
Manufacturing
Liquid crystal displays
Support vector machines
Data structures
Prediction model
Neural networks
Engineers
Multiple regression
Artificial neural network
Compromise
Incomplete data
Support vector machine
Empirical results

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this

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A multi-model approach to determine early manufacturing parameters for small-data-set prediction. / Li, Der-Chiang; Liu, Chiao Wen; Chen, Wen Chih.

In: International Journal of Production Research, Vol. 50, No. 23, 01.12.2012, p. 6679-6690.

Research output: Contribution to journalArticle

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