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Bustillo, Andrés; Reis, Roberto; Machado, Alisson R; Pimenov, Danil Yurievich

Improving the accuracy of machine-learning models with data from machine test repetitions Journal Article

In: Journal of Intelligent Manufacturing, 2020, ISSN: 0956-5515.

Abstract | Links | BibTeX | Tags: Artificial intelligence, Brandsma facing tests, Ensembles, Machine learning, Tool geometry, Turning

Garrido-Labrador, José Luis; Puente-Gabarri, Daniel; Ramirez-Sanz, José Miguel; Ayala-Dulanto, David; Maudes, Jesús

Using Ensembles for Accurate Modelling of Manufacturing Processes in an IoT Data-Acquisition Solution Journal Article

In: Applied Sciences, 10 (13), 2020, ISSN: 2076-3417.

Abstract | Links | BibTeX | Tags: Ensembles, internet of things, Milling, rotation forests, unbalanced datasets


Bustillo, A; Pimenov, D.Yu.; Matuszewski, M; Mikolajczyk, T

Using artificial intelligence models for the prediction of surface wear based on surface isotropy levels Journal Article

In: Robotics and Computer-Integrated Manufacturing, 53 , pp. 215 - 227, 2018, ISSN: 0736-5845.

Abstract | Links | BibTeX | Tags: Ensembles, Isotropy level geometric structure of the surface, Roughness, Small size dataset, Wear

Bustillo, Andres; Urbikain, Gorka; Perez, Jose M; Pereira, Octavio M; de Lacalle, Luis Lopez N

Smart optimization of a friction-drilling process based on boosting ensembles Journal Article

In: Journal of Manufacturing Systems, 2018, ISSN: 0278-6125.

Abstract | Links | BibTeX | Tags: Boosting, Ensembles, Friction drilling, Gap prediction, Small-size dataset


Bustillo, Andres; de Lacalle, Luis López N; Fernández-Valdivielso, Asier; Santos, Pedro

Data-mining modeling for the prediction of wear on forming-taps in the threading of steel components Journal Article

In: Journal of Computational Design and Engineering, 3 (4), pp. 337 - 348, 2016, ISSN: 2288-4300.

Abstract | Links | BibTeX | Tags: Ensembles, Forming taps, Regression trees, Roll taps, Roll-tap wear, Rotation forest, Threading