Izenburua
OptiTwin: Data-Driven Machining Process Optimization Platform for SMEsEgilea
Bertsioa
Postprinta
Eskubideak
© 2024 IEEESarbidea
Sarbide bahituaArgitaratzailearen bertsioa
https://doi.org/10.1109/ETFA61755.2024.10711032Non argitaratua
International Conference on Emerging Technologies and Factory Automation (ETFA) 29. Padova, 10-13 septiembre, 2024Argitaratzailea
IEEEGako-hitzak
Manufacturing industry
Machining
Machine learning
ODS 8 Trabajo decente y crecimiento económico ... [+]
Machining
Machine learning
ODS 8 Trabajo decente y crecimiento económico ... [+]
Manufacturing industry
Machining
Machine learning
ODS 8 Trabajo decente y crecimiento económico
ODS 9 Industria, innovación e infraestructura [-]
Machining
Machine learning
ODS 8 Trabajo decente y crecimiento económico
ODS 9 Industria, innovación e infraestructura [-]
Gaia (UNESCO Tesauroa)
http://vocabularies.unesco.org/thesaurus/concept5767Laburpena
The manufacturing industry is constantly seeking innovative solutions to optimize machining processes. However, there is a lack of efficient digital platforms that fully meet the flexibility, service ... [+]
The manufacturing industry is constantly seeking innovative solutions to optimize machining processes. However, there is a lack of efficient digital platforms that fully meet the flexibility, service composition, and affordability needs of the manufacturing industry, in particular for small and mediumsized enterprises (SMEs). This paper introduces the OptiTwin platform, a novel data-driven system designed to enhance machining process optimization for SMEs. The OptiTwin platform was developed with a focus on data acquisition, management, and analysis based on data driven models. The functionalities of the platform were validated through a drilling use case at Mondragon University's high-performance machining laboratory, demonstrating its effectiveness in real-time tool condition monitoring. The results showcase the potential of OptiTwin in optimizing machining processes and empowering SMEs with data-driven insights for enhanced productivity and quality assurance. [-]
Finantzatzailea
Comisión EuropeaGobierno Vasco
Gobierno Vasco
Programa
Horizon 2020Ikertalde 2022
Ikertalde 2022
Zenbakia
814078IT1519-22
IT1443-22
Laguntzaren URIa
https://doi.org/10.3030/814078Sin información
Sin información
Proiektua
Digital Manufacturing and Design Training Network (DiManD)Ingeniería de Software y Sistemas
Mecanizado de Alto Rendimiento