Employing dependent virtual samples to obtain more manufacturing information in pilot runs

Der Chiang Li, Chien Chih Chen, Wen Chih Chen, Che Jung Chang

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


In most highly competitive manufacturing industries, the sample sizes of new products are usually very small in pilot runs because the production schedules are very tight. To obtain the expected quality in mass production runs using limited data is, therefore, always a challenging issue for engineers. Although machine learning algorithms are widely applied to this task, the training sample size is a key weakness when determining the manufacturing parameters. In order to extract more robust information for engineers from the small datasets, this research, based on regression analysis and fuzzy techniques, develops an effective procedure for new production pattern constructions. In addition, a case study of TFT-LCD manufacturing in 2009 is taken as an example to illustrate the presented approach. The experimental results show that it is possible to develop a robust forecasting model which can provide more precise manufacturing predictions with the limited data acquired from pilot runs.

Original languageEnglish
Pages (from-to)6886-6903
Number of pages18
JournalInternational Journal of Production Research
Issue number23
Publication statusPublished - 2012 Dec 1

All Science Journal Classification (ASJC) codes

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

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