IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i7p1230-d218617.html
   My bibliography  Save this article

Real-Time Idle Time Cancellation by Means of Miniterm 4.0

Author

Listed:
  • Eduardo Garcia

    (Ford Spain, Poligono Industrial Ford S/N, Almussafes, 46440 Valencia, Spain)

  • Nicolás Montés

    (Department of Physics, Mathematics and Computing, University CEU Cardenal Herrera, Alfara del Patriarca, 46115 Valencia, Spain)

Abstract

The paper presents how single-model robotized manufacturing lines are rebalanced to save energy. The key idea is to eliminate idle time that each robot has by means of adjusting the velocity. To do so, the proposed technique predicts the idle time for the next cycle time based on miniterm 4.0. This system measures in real-time the sub-cycle times (mini-terms) with the goal to detect disturbances that predict future machine failures. Mini-terms are used to compute the idle time and the allowed velocity reduction for the Industrial Robot without losing productivity. The proposed predictive control technique has been tested in a real production line located at Ford Almussafes plant (Valencia). The line has six stations where each one has an industrial robot. It is connected to miniterm 4.0 to perform a real test. A discussion, limitations of the technique, future implementations and conclusions are shown at the end of this paper.

Suggested Citation

  • Eduardo Garcia & Nicolás Montés, 2019. "Real-Time Idle Time Cancellation by Means of Miniterm 4.0," Energies, MDPI, vol. 12(7), pages 1-13, March.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1230-:d:218617
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/7/1230/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/7/1230/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Renato Ferrero & Mario Collotta & Maria Victoria Bueno-Delgado & Hsing-Chung Chen, 2020. "Smart Management Energy Systems in Industry 4.0," Energies, MDPI, vol. 13(2), pages 1-3, January.
    2. Javier Llopis & Antonio Lacasa & Eduardo Garcia & Nicolás Montés & Lucía Hilario & Judith Vizcaíno & Cristina Vilar & Judit Vilar & Laura Sánchez & Juan Carlos Latorre, 2022. "Manufacturing Maps, a Novel Tool for Smart Factory Management Based on Petri Nets and Big Data Mini-Terms," Mathematics, MDPI, vol. 10(14), pages 1-22, July.
    3. Riyadh Nazar Ali Algburi & Hongli Gao, 2019. "Health Assessment and Fault Detection System for an Industrial Robot Using the Rotary Encoder Signal," Energies, MDPI, vol. 12(14), pages 1-25, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1230-:d:218617. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.