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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
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    Citations

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    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.

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