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Andrés Bustillo
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2020 – today
- 2024
- [j30]José Alberto Maestro-Prieto, José Miguel Ramírez-Sanz, Andrés Bustillo
, Juan José Rodríguez Diez
Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults. Appl. Intell. 54(6): 4525-4544 (2024) - [j29]Ines Miguel-Alonso, David Checa
Evaluation of the novelty effect in immersive Virtual Reality learning experiences. Virtual Real. 28(1): 27 (2024) - [j28]Henar Guillen-Sanz
A systematic review of wearable biosensor usage in immersive virtual reality experiences. Virtual Real. 28(1): 74 (2024) - [c22]Gadea Lucas-Pérez
Personalising the Training Process with Adaptive Virtual Reality: A Proposed Framework, Challenges, and Opportunities. XR (1) 2024: 376-384 - [c21]Kevin Kastner, Ines Miguel-Alonso, Anjela Mayer, Jean-Rémy Chardonnet, Andrés Bustillo, Jivka Ovtcharova, René Eiswirth, Julian Reichwald:
Evaluating the Effectiveness of Avatar-Based Collaboration in XR for Pump Station Training Scenarios. GeCon 2024: 1-7 - 2023
- [j27]Danil Yurievich Pimenov, Andrés Bustillo
Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review. J. Intell. Manuf. 34(5): 2079-2121 (2023) - [j26]David Checa
Immersive virtual-reality computer-assembly serious game to enhance autonomous learning. Virtual Real. 27(4): 3301-3318 (2023) - [c20]José Miguel Ramírez-Sanz
Detection of Stress Stimuli in Learning Contexts of iVR Environments. XR (2) 2023: 427-440 - 2022
- [j25]David Checa
Using Machine-Learning techniques and Virtual Reality to design cutting tools for energy optimization in milling operations. Int. J. Comput. Integr. Manuf. 35(9): 951-971 (2022) - [j24]Andrés Bustillo
Improving the accuracy of machine-learning models with data from machine test repetitions. J. Intell. Manuf. 33(1): 203-221 (2022) - [c19]David Checa
Collaborative Virtual Reality Environment for Training Load Movement with Overhead Bridge Cranes. XR (1) 2022: 121-129 - 2021
- [j23]Andrés Bustillo
Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth. J. Intell. Manuf. 32(3): 895-912 (2021) - [c18]David Checa
Virtual reality-based tool applied in the teaching and training of condition-based maintenance in induction motors. IECON 2021: 1-6 - 2020
- [j22]David Checa
A review of immersive virtual reality serious games to enhance learning and training. Multim. Tools Appl. 79(9-10): 5501-5527 (2020) - [j21]David Checa
Advantages and limits of virtual reality in learning processes: Briviesca in the fifteenth century. Virtual Real. 24(1): 151-161 (2020) - [c17]Kim Martinez
Considering User Experience Parameters in the Evaluation of VR Serious Games. AVR (1) 2020: 186-193 - [c16]David Checa
A Framework for Educational and Training Immersive Virtual Reality Experiences. AVR (2) 2020: 220-228
2010 – 2019
- 2019
- [j20]Maciej Grzenda, Andrés Bustillo
Semi-supervised roughness prediction with partly unlabeled vibration data streams. J. Intell. Manuf. 30(2): 933-945 (2019) - [c15]David Checa
Virtual Reality Travel Training Simulator for People with Intellectual Disabilities. AVR (1) 2019: 385-393 - 2018
- [j19]Pedro Santos, Jesús Maudes
Identifying maximum imbalance in datasets for fault diagnosis of gearboxes. J. Intell. Manuf. 29(2): 333-351 (2018) - [j18]Danil Yurievich Pimenov, Andrés Bustillo
Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth. J. Intell. Manuf. 29(5): 1045-1061 (2018) - 2017
- [j17]Juan José Rodríguez Diez
A decision-making tool based on decision trees for roughness prediction in face milling. Int. J. Comput. Integr. Manuf. 30(9): 943-957 (2017) - [c14]David Checa
Industrial Heritage Seen Through the Lens of a Virtual Reality Experience. AVR (1) 2017: 116-130 - [c13]Mario Alaguero, David Checa
Measuring the Impact of Low-Cost Short-Term Virtual Reality on the User Experience. AVR (1) 2017: 320-336 - 2016
- [j16]Andrés Bustillo
Interpreting tree-based prediction models and their data in machining processes. Integr. Comput. Aided Eng. 23(4): 349-367 (2016) - [j15]Andrés Bustillo
Data-mining modeling for the prediction of wear on forming-taps in the threading of steel components. J. Comput. Des. Eng. 3(4): 337-348 (2016) - [c12]Blanca Guinea, Mario Alaguero, Fernando Melgosa, Andrés Bustillo
Using a Short Video Animation to Assist with the Diagnosis of Sleep Disorders in Young Children. AVR (2) 2016: 13-29 - [c11]David Checa
Briviesca in the 15th c.: A Virtual Reality Environment for Teaching Purposes. AVR (2) 2016: 126-138 - 2015
- [j14]Andrés Bustillo
A flexible platform for the creation of 3D semi-immersive environments to teach Cultural Heritage. Digit. Appl. Archaeol. Cult. Heritage 2(4): 248-259 (2015) - [j13]Andrés Bustillo
New methodology for the design of ultra-light structural components for machine tools. Int. J. Comput. Integr. Manuf. 28(4): 339-352 (2015) - [j12]Daniel Teixidor, Maciej Grzenda, Andrés Bustillo
Modeling pulsed laser micromachining of micro geometries using machine-learning techniques. J. Intell. Manuf. 26(4): 801-814 (2015) - [j11]Pedro Santos, Luisa F. Villa
An SVM-Based Solution for Fault Detection in Wind Turbines. Sensors 15(3): 5627-5648 (2015) - 2014
- [j10]Andrés Bustillo
Online breakage detection of multitooth tools using classifier ensembles for imbalanced data. Int. J. Syst. Sci. 45(12): 2590-2602 (2014) - 2013
- [j9]Maciej Grzenda, Andrés Bustillo
The evolutionary development of roughness prediction models. Appl. Soft Comput. 13(5): 2913-2922 (2013) - [c10]Pedro Santos, Jesús Maudes
Improvements in Modelling of Complex Manufacturing Processes Using Classification Techniques. IEA/AIE 2013: 664-673 - [c9]Andrés Bustillo
Towards higher machine-tool eco-efficiency with an Information Sharing Platform. IECON 2013: 7548-7553 - 2012
- [j8]Maciej Grzenda, Andrés Bustillo
Improvement of surface roughness models for face milling operations through dimensionality reduction. Integr. Comput. Aided Eng. 19(2): 179-197 (2012) - [j7]Guillem Quintana, Andrés Bustillo
Prediction, monitoring and control of surface roughness in high-torque milling machine operations. Int. J. Comput. Integr. Manuf. 25(12): 1129-1138 (2012) - [j6]Agustín Gajate, Rodolfo E. Haber
Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process. J. Intell. Manuf. 23(3): 869-882 (2012) - [j5]Maciej Grzenda, Andrés Bustillo
A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling. J. Intell. Manuf. 23(5): 1733-1743 (2012) - [j4]Andrés Bustillo
Using artificial intelligence to predict surface roughness in deep drilling of steel components. J. Intell. Manuf. 23(5): 1893-1902 (2012) - [j3]José-Francisco Díez-Pastor, Andrés Bustillo
Boosting Projections to improve surface roughness prediction in high-torque milling operations. Soft Comput. 16(8): 1427-1437 (2012) - [c8]Pedro Santos, Luisa F. Villa
Wind Turbines Fault Diagnosis Using Ensemble Classifiers. ICDM 2012: 67-76 - 2011
- [j2]Andrés Bustillo
Modelling of process parameters in laser polishing of steel components using ensembles of regression trees. Int. J. Comput. Integr. Manuf. 24(8): 735-747 (2011) - [j1]Andrés Bustillo
A Virtual Sensor for Online Fault Detection of Multitooth-Tools. Sensors 11(3): 2773-2795 (2011) - [c7]Andrés Bustillo
Using Ensembles of Regression Trees to Monitor Lubricating Oil Quality. IEA/AIE (1) 2011: 199-206 - [c6]José-Francisco Díez-Pastor, César Ignacio García-Osorio
GRASP Forest: A New Ensemble Method for Trees. MCS 2011: 66-75 - 2010
- [c5]Carlos López Nozal, David H. Martin, Andrés Bustillo, Raúl Marticorena Sánchez:
Final Year Project Management Process. CSEDU (2) 2010: 5-12
2000 – 2009
- 2009
- [c4]Raquel Redondo
A Soft Computing System to Perform Face Milling Operations. IWANN (2) 2009: 1282-1291 - [p1]Andrés Bustillo, Javier Sedano, Leticia Curiel, José Ramón Villar
A Soft Computing System for Modelling the Manufacture of Steel Components. Computer Recognition Systems 3 2009: 601-609 - [e1]Sigeru Omatu, Miguel Rocha, José Bravo, Florentino Fernández Riverola, Emilio Corchado, Andrés Bustillo, Juan M. Corchado:
Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops, Salamanca, Spain, June 10-12, 2009. Proceedings, Part II. Lecture Notes in Computer Science 5518, Springer 2009, ISBN 978-3-642-02480-1 [contents] - 2008
- [c3]Andrés Bustillo
Conventional Methods and AI models for Solving an Industrial an Industrial Problem. EMS 2008: 317-322 - [c2]Andrés Bustillo, Javier Sedano, José Ramón Villar, Leticia Curiel, Emilio Corchado:
AI for Modelling the Laser Milling of Copper Components. IDEAL 2008: 498-507 - 2007
- [c1]Rodolfo E. Haber, Michael Schmittdiel, Angel Alique, Andrés Bustillo, Ramón Galán:
Networked Control Based on Fuzzy Logic. An Application to a High-Performance Milling Process. IWANN 2007: 391-398
Coauthor Index
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