2026-04-21T05:18:04Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/68132024-11-22T07:15:32Zcom_20.500.11984_1143col_20.500.11984_1148

Peralta Abadía, José Joaquín Larrinaga, Felix CUESTA ZABALAJAUREGUI, MIKEL Badiola, Xabier Duo, Aitor Olalde Mendia, Gorka 2024-11-21T15:57:36Z 2024-11-21T15:57:36Z 2024 979-8-3503-6123-0 1946-0759 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=178477 https://hdl.handle.net/20.500.11984/6813 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. eng © 2024 IEEE Manufacturing industry Machining Machine learning ODS 8 Trabajo decente y crecimiento económico ODS 9 Industria, innovación e infraestructura OptiTwin: Data-Driven Machining Process Optimization Platform for SMEs http://purl.org/coar/resource_type/c_c94f