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

研究成果: Article同行評審

12 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)225-233
頁數9
期刊Journal of Intelligent Manufacturing
24
發行號2
DOIs
出版狀態Published - 2013 四月 1

All Science Journal Classification (ASJC) codes

  • 軟體
  • 工業與製造工程
  • 人工智慧

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