A new approach for manufacturing forecast problems with insufficient data: The case of TFT-LCDs

Der Chiang Li, Chih Chieh Chang, Chiao Wen Liu, Wen Chih Chen

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

Manufacturing forecast problems have been widely discussed in recent years, where more accurate predictions could reduce the overall manufacturing costs. This study uses the case of ensuring the heights of thin film transistor-liquid crystal display photo-spacers. It is a small sample size prediction problem, because the data available for analysis is limited on the manufacturing lines. A new approach is developed to deal with this problem, which involves three steps. The first step is using K-means clustering to separate data into K clusters, while the second step is to compute the possibility through the fuzzy membership function in each cluster for attribute extension. The last step is to put the data with new generate attributes into a backpropagation neural network (BPNN) machine learning algorithm. Two performance evaluation methods, cross-validation and data specification testing, are selected to compare the proposed method with three popular prediction models: linear regression, support vector machine for regression (SVR), and BPNN. The results show that the proposed method outperforms the others with regard to the total errors, mean square error, and standard deviation.

Original languageEnglish
Pages (from-to)225-233
Number of pages9
JournalJournal of Intelligent Manufacturing
Volume24
Issue number2
DOIs
Publication statusPublished - 2013 Apr 1

All Science Journal Classification (ASJC) codes

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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