2020
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, vol. 10, no. 13, 2020, ISSN: 2076-3417.
Abstract | Links | BibTeX | Tags: Ensembles, internet of things, Milling, rotation forests, unbalanced datasets
@article{Garrido-Labrador2020,
title = {Using Ensembles for Accurate Modelling of Manufacturing Processes in an IoT Data-Acquisition Solution},
author = {José Luis Garrido-Labrador and Daniel Puente-Gabarri and José Miguel Ramirez-Sanz and David Ayala-Dulanto and Jesús Maudes},
url = {https://www.mdpi.com/2076-3417/10/13/4606/htm},
doi = {https://doi.org/10.3390/app10134606},
issn = {2076-3417},
year = {2020},
date = {2020-07-02},
journal = {Applied Sciences},
volume = {10},
number = {13},
abstract = {The development of complex real-time platforms for the Internet of Things (IoT) opens up a promising future for the diagnosis and the optimization of machining processes. Many issues have still to be solved before IoT platforms can be profitable for small workshops with very flexible workloads and workflows. The main obstacles refer to sensor implementation, IoT architecture, and data processing, and analysis. In this research, the use of different machine-learning techniques is proposed, for the extraction of different information from an IoT platform connected to a machining center, working under real industrial conditions in a workshop. The aim is to evaluate which algorithmic technique might be the best to build accurate prediction models for one of the main demands of workshops: the optimization of machining processes. This evaluation, completed under real industrial conditions, includes very limited information on the machining workload of the machining center and unbalanced datasets. The strategy is validated for the classification of the state of a machining center, its working mode, and the prediction of the thermal evolution of the main machine-tool motors: the axis motors and the milling head motor. The results show the superiority of the ensembles for both classification problems under analysis and all four regression problems. In particular, Rotation Forest-based ensembles turned out to have the best performance in the experiments for all the metrics under study. The models are accurate enough to provide useful conclusions applicable to current industrial practice, such as improvements in machine programming to avoid cutting conditions that might greatly reduce tool lifetime and damage machine components.},
keywords = {Ensembles, internet of things, Milling, rotation forests, unbalanced datasets},
pubstate = {published},
tppubtype = {article}
}
2018
Oleaga, Ibone; Pardo, Carlos; Zulaika, Juan J; Bustillo, Andres
A machine-learning based solution for chatter prediction in heavy-duty milling machines Journal Article
In: Measurement, vol. 128, pp. 34 - 44, 2018, ISSN: 0263-2241.
Abstract | Links | BibTeX | Tags: Chatter, Milling, Polar diagrams, Random forest, Regression trees, Vibrations
@article{OLEAGA201834,
title = {A machine-learning based solution for chatter prediction in heavy-duty milling machines},
author = {Ibone Oleaga and Carlos Pardo and Juan J Zulaika and Andres Bustillo},
url = {http://www.sciencedirect.com/science/article/pii/S0263224118305542},
doi = {https://doi.org/10.1016/j.measurement.2018.06.028},
issn = {0263-2241},
year = {2018},
date = {2018-11-01},
journal = {Measurement},
volume = {128},
pages = {34 - 44},
abstract = {The main productivity constraints of milling operations are self-induced vibrations, especially regenerative chatter vibrations. Two key parameters are linked to these vibrations: the depth of cut achievable without vibrations and the chatter frequency. Both parameters are linked to the dynamics of machine component excitation and the milling operation parameters. Their identification in any cutting direction in milling machine operations requires complex analytical models and mechatronic simulations, usually only applied to identify the worst cutting conditions in operating machines. This work proposes the use of machine learning techniques with no need to calculate the two above-mentioned parameters by means of a 3-step strategy. The strategy combines: 1) experimental frequency responses collected at the tool center point; 2) analytical calculations of both parameters; and, 3) different machine learning techniques. The results of these calculations can then be used to predict chatter under different combinations of milling directions and machine positions. This strategy is validated with real experiments on a bridge milling machine performing concordance roughing operations on AISI 1045 steel with a 125 mm diameter mill fitted with nine cutters at 45°, the results of which have confirmed the high variability of both parameters along the working volume. The following regression techniques are tested: artificial neural networks, regression trees and Random Forest. The results show that Random Forest ensembles provided the highest accuracy with a statistical advantage over the other machine learning models; they achieved a final accuracy of 0.95 mm for the critical depth and 7.3 Hz for the chatter frequency (RMSE) in the whole working volume and in all feed directions, applying a 10 × 10 cross validation scheme. These RMSE values are acceptable from the industrial point of view, taking into account that the critical depth of this range varies between 0.68 mm and 19.20 mm and the chatter frequency between 1.14 Hz and 65.25 Hz. Besides, Random Forest ensembles are more easily optimized than artificial neural networks (1 parameter configuration versus 210 MLPs). Additionally, tools that incorporate regression trees are interesting and highly accurate, providing immediately accessible and useful information in visual formats on critical machine performance for the design engineer.},
keywords = {Chatter, Milling, Polar diagrams, Random forest, Regression trees, Vibrations},
pubstate = {published},
tppubtype = {article}
}