Prediction model to design standard production period for steel plate mills

Masanori Shioya, Kenko Uchida

    研究成果: Conference contribution

    抄録

    Steel plate products are manufactured through many refining processes. Among the refining processes, there are some processes where it is determined during the production whether the plates need to go through, and the uncertainty of the production period and the workload derived by these processes make the production control of the steel plate difficult. In this paper, we propose a method to predict the standard production period that is especially important for the production control. Since black-box models are avoided in the production field, we contrived a model which first predicts the process flow of the refining processes by decision trees and then predicts the probability density function for the production period by adding up the processing periods of the transit processes. These probability density functions for the processing periods are calculated by means of a maximum likelihood estimation under normal distribution assumption, but it was found that the values of the standard production period were not as much different as those under exponential distribution assumption. Moreover, although it does not satisfy the requirements of the production field, we found that the average of the standard production periods improved 0.7 days using quantile regression forest predicting the standard production period directly.

    本文言語English
    ホスト出版物のタイトル2017 3rd International Conference on Control, Automation and Robotics, ICCAR 2017
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ346-351
    ページ数6
    ISBN(電子版)9781509060870
    DOI
    出版ステータスPublished - 2017 6 7
    イベント3rd International Conference on Control, Automation and Robotics, ICCAR 2017 - Nagoya, Japan
    継続期間: 2017 4 222017 4 24

    Other

    Other3rd International Conference on Control, Automation and Robotics, ICCAR 2017
    国/地域Japan
    CityNagoya
    Period17/4/2217/4/24

    ASJC Scopus subject areas

    • 人工知能
    • 制御と最適化
    • 制御およびシステム工学

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