• DocumentCode
    1587291
  • Title

    Visualizing the Yield Pattern Outcome for Automatic Data Exploration

  • Author

    Noor, Megat Norulazmi Megat Mohamed ; Jusoh, Shaidah

  • Author_Institution
    Grad. Dept of Comput. Sci., Univ. Utara Malaysia, Sintok
  • fYear
    2008
  • Firstpage
    404
  • Lastpage
    409
  • Abstract
    Non close loop manufacturing process, typically in the hard disk media industries rely from its inspection machine to generate production yield temporal data that can be used for future analysis. In order for an engineer to proactively perform maintenance on its process equipment and avoiding unnecessary unplanned down time, they need to be able to predict the outcome of the yield before products arrives at the inspection machine. The future prediction of the yield outcome can be achieved by visualizing the historical data pattern generated from the inspection machine, transform the data pattern and map it into machine learning algorithm for training in order to automatically generate a prediction model without the visual interpretation needs to be done by human.
  • Keywords
    data visualisation; inspection; learning (artificial intelligence); maintenance engineering; pattern classification; production engineering computing; automatic data exploration; inspection machine; lazy machine learning algorithm classifier; maintenance engineering; manufacturing yield pattern outcome visualization; nonclose loop manufacturing process; Data engineering; Data mining; Data visualization; Humans; Inspection; Maintenance; Manufacturing industries; Manufacturing processes; Production; Space technology; Automatic data exploration; Data mining; data visualization; machine learning; manufactuirng yield predictive system; predictive system;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Modeling & Simulation, 2008. AICMS 08. Second Asia International Conference on
  • Conference_Location
    Kuala Lumpur
  • Print_ISBN
    978-0-7695-3136-6
  • Electronic_ISBN
    978-0-7695-3136-6
  • Type

    conf

  • DOI
    10.1109/AMS.2008.26
  • Filename
    4530510