Publications

@article{Cruz2022b,

title = {Using Machine-Learning techniques and Virtual Reality to design cutting tools for energy optimization in milling operations},

author = {David Checa Cruz and Gorka Urbikain and Aitor Beranoagirre and Andrés Bustillo and Luis Norberto López Lacalle},

url = {https://www.tandfonline.com/doi/full/10.1080/0951192X.2022.2027020},

doi = {10.1080/0951192X.2022.2027020},

issn = {0951-192X},

year  = {2022},

date = {2022-01-19},

journal = {International Journal of Computer Integrated Manufacturing},

volume = {35},

number = {1},

pages = {1-21},

abstract = {The selection of a proper cutting tool in machining operations is a critical issue. Tool geometric parameters are essential for milling performance. However, the process engineer has very limited experience of the best parameter combination, due to the high cost of cutting tool tests. The same holds true for bachelor studies on machining processes. This study proposes a new strategy that combines experimental tests, machine-learning modelling and Virtual Reality visualization to overcome these limitations. First, tools with different geometric parameters are tested. Second, the experimental data are modeled with different machine-learning techniques (regression trees, multilayer perceptrons, bagging and random forest ensembles). An in-depth analysis of the influence of each input on model accuracy is performed to reduce experimental costs. The results show that the best model with no cutting-force inputs performed worse than the best model with all the inputs. Third, the most accurate model is used to build 3D graphs of special interest to engineering students as well as process engineers, for the optimization of power consumption under different cutting conditions. Finally, a Virtual Reality environment is presented to train engineering students in the study of the best tool design and cutting parameter optimization.},

keywords = {energy optimization, Ensembles, Multilayer perceptron, serrated cutters, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{Bustillo2020,

title = {Improving the accuracy of machine-learning models with data from machine test repetitions},

author = {Andrés Bustillo and Roberto Reis and Alisson R. Machado and Danil Yurievich Pimenov},

url = {https://link.springer.com/article/10.1007%2Fs10845-020-01661-3},

doi = {https://doi.org/10.1007/s10845-020-01661-3},

issn = {0956-5515},

year  = {2020},

date = {2020-09-17},

journal = {Journal of Intelligent Manufacturing},

abstract = {The modelling of machining processes by means of machine-learning algorithms is still based on principles that are especially adapted to mechanical approaches, in which very few inputs are varied with little repetition of experimental conditions. These principles might not be ideal to achieve accurate machine-learning models and they are certainly not aligned with the practicalities of industrial machining in factories. In this research the effect of a new strategy to improve machine-learning model accuracy is studied: experimental repetition. Tool-life prediction in the face-turning operations of AISI 1045 steel discs, depending on different cooling systems and tool geometries, is selected as a case study. Both the side rake and the relief angles of HSS tools are optimized using the Brandsma facing test under dry, MQL, and flooding conditions. Different machine-learning algorithms, such as regression trees, kNNs, artificial neural networks, and ensembles (bagging and Random Forest) are tested. On the one hand, the results of the study showed that artificial neural networks of Radial Basis Functions presented the highest model accuracy (11.4 mm RMSE), but required a very sensitive and complex tuning process. On the other hand, they demonstrated that ensembles, especially Random Forest, provided models with accuracy in the same range, but with no tuning procedure (12.8 mm RMSE). Secondly, the effect of an increased dataset size, by means of experimental repetition, is evaluated and compared with traditional experimental modelling that used average values. The results showed that some machine-learning techniques, including both ensemble types, significantly improved their accuracy with this strategy, by up to 23%. The results therefore suggested that the use of raw experimental data, rather than their averaged values, can achieve machine-learning models of higher accuracy for tool-wear processes.},

keywords = {Artificial intelligence, Brandsma facing tests, Ensembles, Machine learning, Tool geometry, Turning},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{BUSTILLO2018215,

title = {Using artificial intelligence models for the prediction of surface wear based on surface isotropy levels},

author = {A Bustillo and D. Yu. Pimenov and M Matuszewski and T Mikolajczyk},

url = {http://www.sciencedirect.com/science/article/pii/S0736584517303733},

doi = {https://doi.org/10.1016/j.rcim.2018.03.011},

issn = {0736-5845},

year  = {2018},

date = {2018-10-01},

journal = {Robotics and Computer-Integrated Manufacturing},

volume = {53},

pages = {215 - 227},

abstract = {Currently, a key industrial challenge in friction processes is the prediction of surface roughness and loss of mass under different machining processes, such as Electro-Discharge Machining (EDM), and turning and grinding processes. Under industrial conditions, only the sliding distance is easily evaluated in friction processes, while the acquisition of other variables usually implies expensive costs for production centres, such as the integration of sensors in functioning machine-tools. Besides, appropriate datasets are usually very small, because the testing of different friction conditions is also expensive. These two restrictions, small datasets and very few inputs, make it very difficult to use Artificial Intelligence (AI) techniques to model the industrial problem. So, the use of the isotropy level of the surface structure is proposed, as another input that is easily evaluated prior to the friction process. In this example, the friction processes of a cubic sample of 102Cr6 (40 HRC) steel and a further element made of X210Cr12 (60 HRC) steel are considered. Different artificial intelligence techniques, such as artificial regression trees, multilayer perceptrons (MLPs), radial basis networks (RBFs), and Random Forest, were tested considering the isotropy level as either a nominal or a numeric attribute, to evaluate improvements in the accuracy of surface roughness and loss-of-mass predictions. The results obtained with real datasets showed that RBFs and MLPs provided the most accurate models for loss of mass and surface roughness prediction, respectively. MLPs have slightly higher surface prediction accuracy than Random Forest, although MLP models are very sensitive to the tuning of their parameters (a small mismatch between the learning rate and the momentum in the MLP will drastically reduce the accuracy of the model). In contrast, Random Forest has no parameter to be tuned and its prediction is almost as good as MLPs for surface roughness, so Random Forest will be more suitable for industrial use where no expert in AI model tuning is available. Moreover, the inclusion of the isotropy level in the dataset, especially as a numeric attribute, greatly improved the accuracy of the models, in some cases, by up to 52% for MLPs, and by a smaller proportion of 16% in the Random Forest models in terms of Root Mean Square Error. Finally, Random Forest ensembles only trained with low and very high isotropy level experimental datasets generated reliable models for medium levels of isotropy, thereby offering a solution to reduce the size of training datasets.},

keywords = {Ensembles, Isotropy level geometric structure of the surface, Roughness, Small size dataset, Wear},

pubstate = {published},

tppubtype = {article}

}

@article{BUSTILLO2018b,

title = {Smart optimization of a friction-drilling process based on boosting ensembles},

author = {Andres Bustillo and Gorka Urbikain and Jose M Perez and Octavio M Pereira and Luis Lopez N Lacalle},

url = {http://www.sciencedirect.com/science/article/pii/S0278612518301249},

doi = {https://doi.org/10.1016/j.jmsy.2018.06.004},

issn = {0278-6125},

year  = {2018},

date = {2018-08-16},

journal = {Journal of Manufacturing Systems},

abstract = {Form and friction drilling techniques are now promising alternatives in light and medium boilermaking that will very probably supersede conventional drilling techniques, as rapid and economic solutions for producing nutless bolted joints. Nonetheless, given the number of cutting parameters involved, optimization of the process requires calibration of the main input parameters in relation to the desired output values. Among these values, the gap between plates determines the service life of the joint. In this paper, a suitable smart manufacturing strategy for real industrial conditions is proposed, where it is necessary to identify the most accurate machine-learning technique to process experimental datasets of a small size. The strategy is first to generate a small-size dataset under real industrial conditions, then the gap is discretized taking into account the specific industrial needs of this quality indicator for each product. Finally, the different machine learning models are tested and fine-tuned to ascertain the most accurate model at the lowest cost. The strategy is validated with a 48 condition-dataset where only feed-rate and rotation speed are used as inputs and the gap as the output. The results on this dataset showed that the Adaboost ensembles provided the highest accuracy and were more easily optimized than artificial neural networks.},

keywords = {Boosting, Ensembles, Friction drilling, Gap prediction, Small-size dataset},

pubstate = {published},

tppubtype = {article}

}

@article{BUSTILLO2016337,

title = {Data-mining modeling for the prediction of wear on forming-taps in the threading of steel components},

author = {Andres Bustillo and Luis López N Lacalle and Asier Fernández-Valdivielso and Pedro Santos},

url = {http://www.sciencedirect.com/science/article/pii/S2288430016300306},

doi = {https://doi.org/10.1016/j.jcde.2016.06.002},

issn = {2288-4300},

year  = {2016},

date = {2016-10-01},

journal = {Journal of Computational Design and Engineering},

volume = {3},

number = {4},

pages = {337 - 348},

abstract = {An experimental approach is presented for the measurement of wear that is common in the threading of cold-forged steel. In this work, the first objective is to measure wear on various types of roll taps manufactured to tapping holes in microalloyed HR45 steel. Different geometries and levels of wear are tested and measured. Taking their geometry as the critical factor, the types of forming tap with the least wear and the best performance are identified. Abrasive wear was observed on the forming lobes. A higher number of lobes in the chamber zone and around the nominal diameter meant a more uniform load distribution and a more gradual forming process. A second objective is to identify the most accurate data-mining technique for the prediction of form-tap wear. Different data-mining techniques are tested to select the most accurate one: from standard versions such as Multilayer Perceptrons, Support Vector Machines and Regression Trees to the most recent ones such as Rotation Forest ensembles and Iterated Bagging ensembles. The best results were obtained with ensembles of Rotation Forest with unpruned Regression Trees as base regressors that reduced the RMS error of the best-tested baseline technique for the lower length output by 33%, and Additive Regression with unpruned M5P as base regressors that reduced the RMS errors of the linear fit for the upper and total lengths by 25% and 39%, respectively. However, the lower length was statistically more difficult to model in Additive Regression than in Rotation Forest. Rotation Forest with unpruned Regression Trees as base regressors therefore appeared to be the most suitable regressor for the modeling of this industrial problem.},

keywords = {Ensembles, Forming taps, Regression trees, Roll taps, Roll-tap wear, Rotation forest, Threading},

pubstate = {published},

tppubtype = {article}

}