Digitalization of an Industrial Process for Bearing Production
The developments in sensing, actuation, and algorithms, both in terms of Artificial Intelligence (AI) and data treatment, have open up a wide range of possibilities for improving the quality of the production systems in diverse industrial fields. The present paper describes the automatizing process...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7783 |
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| author | Jose-Manuel Rodriguez-Fortun Jorge Alvarez Luis Monzon Ricardo Salillas Sergio Noriega David Escuin David Abadia Aitor Barrutia Victor Gaspar Jose Antonio Romeo Fernando Cebrian Rafael del-Hoyo-Alonso |
| author_facet | Jose-Manuel Rodriguez-Fortun Jorge Alvarez Luis Monzon Ricardo Salillas Sergio Noriega David Escuin David Abadia Aitor Barrutia Victor Gaspar Jose Antonio Romeo Fernando Cebrian Rafael del-Hoyo-Alonso |
| author_sort | Jose-Manuel Rodriguez-Fortun |
| collection | DOAJ |
| description | The developments in sensing, actuation, and algorithms, both in terms of Artificial Intelligence (AI) and data treatment, have open up a wide range of possibilities for improving the quality of the production systems in diverse industrial fields. The present paper describes the automatizing process performed in a production line for high-quality bearings. The actuation considered new sensing elements at the machine level and the treatment of the information, fusing the different sources in order to detect quality defects in the grinding process (waviness, burns) and monitoring the state of the tool. At a supervision level, an AI model has been developed for monitoring the complete line and compensating deviations in the dimension of the final assembly. The project also contemplated the hardware architecture for improving the data acquisition and communication among the machines and databases, the data treatment units, and the human interfaces. The resulting system gives feedback to the operator when deviations or potential errors are detected so that the quality issues are recognized and can be amended in advance, thereby reducing the quality cost. |
| format | Article |
| id | doaj-art-3ba820ef694145468536eb7deec77e44 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3ba820ef694145468536eb7deec77e442025-08-20T02:50:37ZengMDPI AGSensors1424-82202024-12-012423778310.3390/s24237783Digitalization of an Industrial Process for Bearing ProductionJose-Manuel Rodriguez-Fortun0Jorge Alvarez1Luis Monzon2Ricardo Salillas3Sergio Noriega4David Escuin5David Abadia6Aitor Barrutia7Victor Gaspar8Jose Antonio Romeo9Fernando Cebrian10Rafael del-Hoyo-Alonso11Technological Institute of Aragón, Calle Maria de Luna, 7-8, 50018 Zaragoza, SpainIdeko S.Coop, Member of Basque Research and Technology Alliance (BRTA), Arriaga Kalea, 2, 20870 Elgoibar, Gipuzkoa, SpainTechnological Institute of Aragón, Calle Maria de Luna, 7-8, 50018 Zaragoza, SpainTechnological Institute of Aragón, Calle Maria de Luna, 7-8, 50018 Zaragoza, SpainTechnological Institute of Aragón, Calle Maria de Luna, 7-8, 50018 Zaragoza, SpainTechnological Institute of Aragón, Calle Maria de Luna, 7-8, 50018 Zaragoza, SpainTechnological Institute of Aragón, Calle Maria de Luna, 7-8, 50018 Zaragoza, SpainIdeko S.Coop, Member of Basque Research and Technology Alliance (BRTA), Arriaga Kalea, 2, 20870 Elgoibar, Gipuzkoa, SpainFersa Bearings, Calle Bari 37, 50197 Zaragoza, SpainFersa Bearings, Calle Bari 37, 50197 Zaragoza, SpainFersa Bearings, Calle Bari 37, 50197 Zaragoza, SpainTechnological Institute of Aragón, Calle Maria de Luna, 7-8, 50018 Zaragoza, SpainThe developments in sensing, actuation, and algorithms, both in terms of Artificial Intelligence (AI) and data treatment, have open up a wide range of possibilities for improving the quality of the production systems in diverse industrial fields. The present paper describes the automatizing process performed in a production line for high-quality bearings. The actuation considered new sensing elements at the machine level and the treatment of the information, fusing the different sources in order to detect quality defects in the grinding process (waviness, burns) and monitoring the state of the tool. At a supervision level, an AI model has been developed for monitoring the complete line and compensating deviations in the dimension of the final assembly. The project also contemplated the hardware architecture for improving the data acquisition and communication among the machines and databases, the data treatment units, and the human interfaces. The resulting system gives feedback to the operator when deviations or potential errors are detected so that the quality issues are recognized and can be amended in advance, thereby reducing the quality cost.https://www.mdpi.com/1424-8220/24/23/7783Industry 4.0digitalizationwavinessburnsmachine learninggrinding |
| spellingShingle | Jose-Manuel Rodriguez-Fortun Jorge Alvarez Luis Monzon Ricardo Salillas Sergio Noriega David Escuin David Abadia Aitor Barrutia Victor Gaspar Jose Antonio Romeo Fernando Cebrian Rafael del-Hoyo-Alonso Digitalization of an Industrial Process for Bearing Production Sensors Industry 4.0 digitalization waviness burns machine learning grinding |
| title | Digitalization of an Industrial Process for Bearing Production |
| title_full | Digitalization of an Industrial Process for Bearing Production |
| title_fullStr | Digitalization of an Industrial Process for Bearing Production |
| title_full_unstemmed | Digitalization of an Industrial Process for Bearing Production |
| title_short | Digitalization of an Industrial Process for Bearing Production |
| title_sort | digitalization of an industrial process for bearing production |
| topic | Industry 4.0 digitalization waviness burns machine learning grinding |
| url | https://www.mdpi.com/1424-8220/24/23/7783 |
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