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Detailed Energy Analysis of a Sheet-Metal-Forming Press from Electrical Measurements

Author

Listed:
  • Camilo Carrillo

    (Research Group on Efficient and Digital Engineering, University of Vigo, 36310 Vigo, Spain)

  • Eloy Díaz Dorado

    (Research Group on Efficient and Digital Engineering, University of Vigo, 36310 Vigo, Spain)

  • José Cidrás Pidre

    (Research Group on Efficient and Digital Engineering, University of Vigo, 36310 Vigo, Spain)

  • Julio Garrido Campos

    (Research Group on Efficient and Digital Engineering, University of Vigo, 36310 Vigo, Spain)

  • Diego San Facundo López

    (Research Group on Efficient and Digital Engineering, University of Vigo, 36310 Vigo, Spain)

  • Luiz A. Lisboa Cardoso

    (Research Group on Efficient and Digital Engineering, University of Vigo, 36310 Vigo, Spain)

  • Cristina I. Martínez Castañeda

    (Stellantis Group, 36210 Vigo, Spain)

  • José F. Sánchez Rúa

    (Stellantis Group, 36210 Vigo, Spain)

Abstract

This paper presents a methodology that allows for the detection of the state of a sheet-metal-forming press, the parts being produced, their cadence, and the energy demand for each unit produced. For this purpose, only electrical measurements are used. The proposed analysis is conducted at the level of the press subsystems: main motor, transfer module, cushion, and auxiliary systems, and is intended to count, classify, and monitor the production of pressed parts. The power data are collected every 20 ms and show cyclic behavior, which is the basis for the presented methodology. A neural network (NN) based on heuristic rules is developed to estimate the press states. Then, the production period is determined from the power data using a least squares method to obtain normalized harmonic coefficients. These are the basis for a second NN dedicated to identifying the parts in production. The global error in estimating the parts being produced is under 1%. The resulting information could be handy in determining relevant information regarding the press behavior, such as energy per part, which is necessary in order to evaluate the energy performance of the press under different production conditions.

Suggested Citation

  • Camilo Carrillo & Eloy Díaz Dorado & José Cidrás Pidre & Julio Garrido Campos & Diego San Facundo López & Luiz A. Lisboa Cardoso & Cristina I. Martínez Castañeda & José F. Sánchez Rúa, 2023. "Detailed Energy Analysis of a Sheet-Metal-Forming Press from Electrical Measurements," Energies, MDPI, vol. 16(19), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6972-:d:1254545
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    References listed on IDEAS

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    1. Li, Chuyi & Zheng, Kedi & Guo, Hongye & Chen, Qixin, 2023. "A mixed-integer programming approach for industrial non-intrusive load monitoring," Applied Energy, Elsevier, vol. 330(PA).
    2. Carrie Armel, K. & Gupta, Abhay & Shrimali, Gireesh & Albert, Adrian, 2013. "Is disaggregation the holy grail of energy efficiency? The case of electricity," Energy Policy, Elsevier, vol. 52(C), pages 213-234.
    3. Cristina Puente & Rafael Palacios & Yolanda González-Arechavala & Eugenio Francisco Sánchez-Úbeda, 2020. "Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques," Energies, MDPI, vol. 13(12), pages 1-20, June.
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