Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/24/13187 |
| _version_ | 1797382167213375488 |
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| author | Mariluz Penalva Ander Martín Cristina Ruiz Víctor Martínez Fernando Veiga Alain Gil del Val Tomás Ballesteros |
| author_facet | Mariluz Penalva Ander Martín Cristina Ruiz Víctor Martínez Fernando Veiga Alain Gil del Val Tomás Ballesteros |
| author_sort | Mariluz Penalva |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T21:01:31Z |
| format | Article |
| id | doaj.art-55b5faa54da2454298d1ee4d6ff8839e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T21:01:31Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-55b5faa54da2454298d1ee4d6ff8839e2023-12-22T13:51:47ZengMDPI AGApplied Sciences2076-34172023-12-0113241318710.3390/app132413187Application-Oriented Data Analytics in Large-Scale Metal Sheet BendingMariluz Penalva0Ander Martín1Cristina Ruiz2Víctor Martínez3Fernando Veiga4Alain Gil del Val5Tomás Ballesteros6TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Científico, Parque Científico y Tecnológico de Gipuzkoa, 20009 Donostia-San Sebastián, SpainTECNALIA, Basque Research and Technology Alliance (BRTA), Parque Científico, Parque Científico y Tecnológico de Gipuzkoa, 20009 Donostia-San Sebastián, SpainIDESA Ingeniería y Diseño Europeo, PCTG. Edificio Félix Herreros, 33203 Gijón, SpainIDESA Ingeniería y Diseño Europeo, PCTG. Edificio Félix Herreros, 33203 Gijón, SpainDepartment of Engineering, Campus Arrosadía, Public University of Navarre, Los Pinos Building, 31006 Pamplona, SpainTECNALIA, Basque Research and Technology Alliance (BRTA), Parque Científico, Parque Científico y Tecnológico de Gipuzkoa, 20009 Donostia-San Sebastián, SpainDepartment of Engineering, Campus Arrosadía, Public University of Navarre, Los Pinos Building, 31006 Pamplona, SpainThe 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.https://www.mdpi.com/2076-3417/13/24/13187rollingmonitoringdeep learningneuronal networksmaterial deformation |
| spellingShingle | Mariluz Penalva Ander Martín Cristina Ruiz Víctor Martínez Fernando Veiga Alain Gil del Val Tomás Ballesteros Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending Applied Sciences rolling monitoring deep learning neuronal networks material deformation |
| title | Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending |
| title_full | Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending |
| title_fullStr | Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending |
| title_full_unstemmed | Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending |
| title_short | Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending |
| title_sort | application oriented data analytics in large scale metal sheet bending |
| topic | rolling monitoring deep learning neuronal networks material deformation |
| url | https://www.mdpi.com/2076-3417/13/24/13187 |
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