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Article

Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending

by
Mariluz Penalva
1,
Ander Martín
1,
Cristina Ruiz
2,
Víctor Martínez
2,
Fernando Veiga
3,*,
Alain Gil del Val
1,4 and
Tomás Ballesteros
3
1
TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Científico, Parque Científico y Tecnológico de Gipuzkoa, 20009 Donostia-San Sebastián, Spain
2
IDESA Ingeniería y Diseño Europeo, PCTG. Edificio Félix Herreros, 33203 Gijón, Spain
3
Department of Engineering, Campus Arrosadía, Public University of Navarre, Los Pinos Building, 31006 Pamplona, Spain
4
School of Engineering and Technology, International University of La Rioja UNIR, 26006 Logroño, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(24), 13187; https://doi.org/10.3390/app132413187
Submission received: 27 October 2023 / Revised: 30 November 2023 / Accepted: 8 December 2023 / Published: 12 December 2023
(This article belongs to the Special Issue Advanced Metal Forming and Smart Manufacturing Processes)
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Abstract

The sheet-metal-forming process is crucial in manufacturing various products, including pipes, cans, and containers. Despite its significance, controlling this complex process is challenging and may lead to defects and inefficiencies. This study introduces a novel approach to monitor the sheet-metal-forming process, specifically focusing on the rolling of cans in the oil-and-gas sector. The methodology employed in this work involves the application of temporal-signal-processing and artificial-intelligence (AI) techniques for monitoring and optimizing the manufacturing process. Temporal-signal-processing techniques, such as Markov transition fields (MTFs), are utilized to transform time series data into images, enabling the identification of patterns and anomalies. synamic time warping (DTW) aligns time series data, accommodating variations in speed or timing across different rolling processes. K-medoids clustering identifies representative points, characterizing distinct phases of the rolling process. The results not only demonstrate the effectiveness of this framework in monitoring the rolling process but also lay the foundation for the practical application of these methodologies. This allows operators to work with a simpler characterization source, facilitating a more straightforward interpretation of the manufacturing process.
Keywords: rolling; monitoring; deep learning; neuronal networks; material deformation rolling; monitoring; deep learning; neuronal networks; material deformation

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MDPI and ACS Style

Penalva, M.; Martín, A.; Ruiz, C.; Martínez, V.; Veiga, F.; Val, A.G.d.; Ballesteros, T. Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending. Appl. Sci. 2023, 13, 13187. https://doi.org/10.3390/app132413187

AMA Style

Penalva M, Martín A, Ruiz C, Martínez V, Veiga F, Val AGd, Ballesteros T. Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending. Applied Sciences. 2023; 13(24):13187. https://doi.org/10.3390/app132413187

Chicago/Turabian Style

Penalva, Mariluz, Ander Martín, Cristina Ruiz, Víctor Martínez, Fernando Veiga, Alain Gil del Val, and Tomás Ballesteros. 2023. "Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending" Applied Sciences 13, no. 24: 13187. https://doi.org/10.3390/app132413187

APA Style

Penalva, M., Martín, A., Ruiz, C., Martínez, V., Veiga, F., Val, A. G. d., & Ballesteros, T. (2023). Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending. Applied Sciences, 13(24), 13187. https://doi.org/10.3390/app132413187

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