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

Camilo Carrillo (), Eloy Díaz Dorado, José Cidrás Pidre, Julio Garrido Campos, Diego San Facundo López, Luiz A. Lisboa Cardoso, Cristina I. Martínez Castañeda and José F. Sánchez Rúa
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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

Energies, 2023, vol. 16, issue 19, 1-17

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.

Keywords: industrial machines; energy patterns; nonintrusive load monitoring; artificial neural networks; part classification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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