Zerrendatu honen arabera: egilea "f9cfd74881df9f65494b7ae6f0b14c53"
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Active Power Optimization of a Turning Process by Cutting Conditions Selection: A Q-Learning Approach
Duo, Aitor; Reguera-Bakhache, Daniel; Izagirre, Unai; Aperribay Zubia, Javier (IEEE, 2022)In the context of Industry 4.0, the optimization of manufacturing processes is a challenge. Although in recent years many of the efforts have been in this direction, there is still improvement opportunities in these ... -
The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process
Duo, Aitor; Basagoiti, Rosa; ARRAZOLA, PEDRO JOSE; Aperribay Zubia, Javier; CUESTA ZABALAJAUREGUI, MIKEL (Springer Verlag, 2019)Industrial processes are being developed under a new scenario based on the digitalisation of manufacturing processes.Through this, it is intended to improve the management of resources, decision-making, ... -
Data-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insights
Duo, Aitor; Aguirre, Aitor (MDPI, 2024)PEO constitutes a promising surface technology for the development of protective and functional ceramic coatings on lightweight alloys. Despite its interesting advantages, including enhanced wear and corrosion resistances ... -
Drilling process monitoring: A framework for data gathering and feature extraction techniques
Duo, Aitor; Basagoiti, Rosa; ARRAZOLA, PEDRO JOSE (Elsevier B.V., 2021) -
Drilling test data from new and worn bits
Duo, Aitor; Basagoiti, Rosa; ARRAZOLA, PEDRO JOSE; Aperribay Zubia, Javier; CUESTA ZABALAJAUREGUI, MIKEL (2019)This directory contains the raw data acquired by Mondragon Unibertsitatea during the execution of drilling tests. These data were used to obtain the results presented in the article "The capacity of statistical features ... -
Estimación cualitativa de la rugosidad mediante algoritmos de aprendizaje automático en una operación de taladrado
Duo, Aitor; Dominguez Romero, Erika; Azpitarte-Aranzabal, Larraitz; Aperribay Zubia, Javier; CUESTA ZABALAJAUREGUI, MIKEL; Garay, Ainara; Basagoiti, Rosa; ARRAZOLA, PEDRO JOSE (Federación de Asociaciones de Ingenieros Industriales de España, 2020) -
OptiTwin: Data-Driven Machining Process Optimization Platform for SMEs
Peralta Abadía, José Joaquín; Larrinaga, Felix; CUESTA ZABALAJAUREGUI, MIKEL; Badiola, Xabier; Duo, Aitor; Olalde Mendia, Gorka (IEEE, 2024)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 ... -
Sensor and CNC internal signal evaluation to detect tool and workpiece malfunctions in the drilling process
Duo, Aitor (Mondragon Unibertsitatea. Goi Eskola Politeknikoa, 2021)Lehiakortasuna bultzatzeko eta bezeroen eskaera aldakorrei erantzuteko, fabrikazio sektorea Informazio eta Komunikazio Teknologiak (IKT) aprobetxatzen ari da. Mekanizazioa ez da salbuespena, eta mekanizazio prozesuak sare ... -
Sensor signal selection for tool wear curve estimation and subsequent tool breakage prediction in a drilling operation
Duo, Aitor; Basagoiti, Rosa; ARRAZOLA, PEDRO JOSE; CUESTA ZABALAJAUREGUI, MIKEL (Taylor & Francis, 2021)Tool condition monitoring have an important role in machining processes to reduce defective component and ensure quality requirements. Stopping the process before the tool breaks or an excessive tool wear is reached can ... -
Surface roughness assessment on hole drilled through the identification and clustering of relevant external and internal signal statistical features
Duo, Aitor; Basagoiti, Rosa; ARRAZOLA, PEDRO JOSE; CUESTA ZABALAJAUREGUI, MIKEL; Illarramendi, Miren (Elsevier, 2022)Drilling is a continuous cutting process where two or more cutting edges remove the material, to obtain the desired feature. During the chip evacuation, it generally rubs against the generated surface. Thus, the roughness ...
