| The capacity of statistical features extracted from multiple signals to predict tool wear in the drilling process A Duo, R Basagoiti, PJ Arrazola, J Aperribay, M Cuesta The International Journal of Advanced Manufacturing Technology 102, 2133-2146, 2019 | 25 | 2019 |
| Drilling process monitoring: a framework for data gathering and feature extraction techniques A Duo, T Segreto, A Caggiano, R Basagoiti, R Teti, PJ Arrazola Procedia CIRP 99, 189-195, 2021 | 12 | 2021 |
| Sensor signal selection for tool wear curve estimation and subsequent tool breakage prediction in a drilling operation A Duo, R Basagoiti, PJ Arrazola, M Cuesta International Journal of Computer Integrated Manufacturing 35 (2), 203-227, 2022 | 5 | 2022 |
| Surface roughness assessment on hole drilled through the identification and clustering of relevant external and internal signal statistical features A Duo, R Basagoiti, PJ Arrazola, M Cuesta, M Illarramendi CIRP Journal of Manufacturing Science and Technology 36, 143-157, 2022 | 5 | 2022 |
| A comparative study between internal and external signals for tool wear detection in drilling processes A Duo, R Basagoiti, P J-Arrazola, J Aperribay 14th international conference on high speed machining, 2018 | 2 | 2018 |
| Active Power Optimization of a Turning Process by Cutting Conditions Selection: A Q-Learning Approach A Duo, D Reguera-Bakhache, U Izaguirre, J Aperribay 2022 IEEE 27th International Conference on Emerging Technologies and Factory …, 2022 | 1 | 2022 |
| Intelligent machine tool monitoring and control system AD Zubiaurre, MI Rezabal, RB Astigarraga, PAAEH Fernandez, JA Zubia, ... Jornadas SARTECO, Salamanca, 2016 | 1 | 2016 |
| Estimación cualitativa de la rugosidad mediante algoritmos de aprendizaje automático en una operación de taladrado AD Zubiaurre, ED Romero, LA Aranzabal, JA Zubia, MC Zabaljauregui, ... DYNA 95 (5), 487-491, 2020 | | 2020 |
| Qualitative estimation of roughness using automatic learning algorithms in a drilling operation A Duo Zubiaurre, E Dominguez Romero, L Azpitarte-Aranzabal, ... DYNA 95 (5), 487-491, 2020 | | 2020 |
| Drilling test data from new and worn bits A Duo, R Basagoiti, PJ ARRAZOLA, J Aperribay Zubia, ... | | 2019 |
| A Novel Reinforcement Learning Based Maintenance Scheduling Framework for Real Manufacturing Assembly Lines U Izagirre, A Duo, D Reguera-Bakhache Available at SSRN 4391741, 0 | | |
Pedro-J. Arrazola (Orcid : 0000-0001-...Mondragon Unibertsitaeta, Faculty of EngineeringVerified email at mondragon.edu
