Publications

@article{Cerro2021,
title = {Use of machine learning algorithms for surface roughness prediction of printed parts in polyvinyl butyral via fused deposition modeling},
author = {Azahara Cerro and Pablo E. Romero and Okan Yiğit and Andrés Bustillo},
url = {https://link.springer.com/article/10.1007/s00170-021-07300-2},
doi = {https://doi.org/10.1007/s00170-021-07300-2},
issn = {0268-3768},
year = {2021},
date = {2021-05-25},
journal = {The International Journal of Advanced Manufacturing Technology},
abstract = {Machine learning algorithms for classification are employed in this study to generate different models that can predict the surface roughness of parts manufactured from polyvinyl butyral by means of Fused Deposition Modeling (FDM). Five input variables are defined (layer height, print speed, number of perimeters, wall angle, and extruder temperature), and 16 parts are 3D printed, each with three different surfaces (48 surfaces in total). The print values used to print each part were defined by a fractionated orthogonal experimental design. Using a perthometer, the average value of surface roughness, Ra, on each surface was obtained. From these experimental values, 40 models were trained and validated. The model with the best prediction results was the one generated by bagging and Multilayer Perceptron (BMLP), with a Kappa statistic of 0.9143. The input variables with the highest influence on the surface finish are the wall angle and the layer height.},
keywords = {3d printing, Fused deposition modeling Fused filament fabrication, Machine learning, surface roughness, WEKA},
pubstate = {published},
tppubtype = {article}
}

@article{Pimenov2019,
title = {Effect of the Relative Position of the Face Milling Tool towards the Workpiece on Machined Surface Roughness and Milling Dynamics},
author = {Danil Yurievich Pimenov and Amauri Hassui and Szymon Wojciechowski and Mozammel Mia and Aristides Magri and Daniel I. Suyama and Andrés Bustillo and Grzegorz Krolczyk and Munish Kumar Gupta},
doi = {10.3390/app9050842},
issn = {2076-3417},
year = {2019},
date = {2019-02-27},
journal = {Applied Sciences},
volume = {9},
number = {5},
pages = {842},
abstract = {In face milling one of the most important parameters of the process quality is the roughness of the machined surface. In many articles, the influence of cutting regimes on the roughness and cutting forces of face milling is considered. However, during flat face milling with the milling width B lower than the cutter’s diameter D, the influence of such an important parameter as the relative position of the face mill towards the workpiece and the milling kinematics (Up or Down milling) on the cutting force components and the roughness of the machined surface has not been sufficiently studied. At the same time, the values of the cutting force components can vary significantly depending on the relative position of the face mill towards the workpiece, and thus have a different effect on the power expended on the milling process. Having studied this influence, it is possible to formulate useful recommendations for a technologist who creates a technological process using face milling operations. It is possible to choose such a relative position of the face mill and workpiece that will provide the smallest value of the surface roughness obtained by face milling. This paper shows the influence of the relative position of the face mill towards the workpiece and milling kinematics on the components of the cutting forces, the acceleration of the machine spindle in the process of face milling (considering the rotation of the mill for a full revolution), and on the surface roughness obtained by face milling. Practical recommendations on the assignment of the relative position of the face mill towards the workpiece and the milling kinematics are given.},
keywords = {acceleration, cutting force, Face milling, relative position, surface roughness},
pubstate = {published},
tppubtype = {article}
}

@article{Pimenov2018,
title = {Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth},
author = {Yu. D Pimenov and A Bustillo and T Mikolajczyk},
url = {https://doi.org/10.1007/s10845-017-1381-8},
doi = {10.1007/s10845-017-1381-8},
issn = {1572-8145},
year = {2018},
date = {2018-06-01},
journal = {Journal of Intelligent Manufacturing},
volume = {29},
number = {5},
pages = {1045--1061},
abstract = {Nowadays, face milling is one of the most widely used machining processes for the generation of flat surfaces. Following international standards, the quality of a machined surface is measured in terms of surface roughness, Ra, a parameter that will decrease with increased tool wear. So, cutting inserts of the milling tool have to be changed before a given surface quality threshold is exceeded. The use of artificial intelligence methods is suggested in this paper for real-time prediction of surface roughness deviations, depending on the main drive power, and taking tool wear, $$V_B$$ V B into account. This method ensures comprehensive use of the potential of modern CNC machines that are able to monitor the main drive power, N, in real-time. It can likewise estimate the three parameters -maximum tool wear, machining time, and cutting power- that are required to generate a given surface roughness, thereby making the most efficient use of the cutting tool. A series of artificial intelligence methods are tested: random forest (RF), standard Multilayer perceptrons (MLP), Regression Trees, and radial-based functions. Random forest was shown to have the highest model accuracy, followed by regression trees, displaying higher accuracy than the standard MLP and the radial-basis function. Moreover, RF techniques are easily tuned and generate visual information for direct use by the process engineer, such as the linear relationships between process parameters and roughness, and thresholds for avoiding rapid tool wear. All of this information can be directly extracted from the tree structure or by drawing 3D charts plotting two process inputs and the predicted roughness depending on workshop requirements.},
keywords = {Cutting power, Face milling Wear, Processing time, Random forest, surface roughness},
pubstate = {published},
tppubtype = {article}
}

@article{Rodríguez2017,
title = {A decision-making tool based on decision trees for roughness prediction in face milling},
author = {Juan José Rodríguez and Guillem Quintana and Andrés Bustillo and Joaquim Ciurana},
url = {https://www.tandfonline.com/doi/full/10.1080/0951192X.2016.1247991},
doi = {10.1080/0951192X.2016.1247991},
issn = {0951-192X},
year = {2017},
date = {2017-01-01},
journal = {International Journal of Computer Integrated Manufacturing},
volume = {30},
number = {9},
abstract = {The selection of the right cutting tool in manufacturing process design is always an open question, especially when different tools are available on the market with similar characteristics, but marked differences in price, ranging from low-cost to high-performance cutting tools. The ultimate decision of the engineer will depend on previous experience with the life cycle of the tool and its performance, but without the support of a systematic knowledge base. This research presents a decision-making system based on soft-computing techniques. First, several experiments were carried out with four different cutting tools: two flat-milling low-cost tools without any surface treatment or coating and two high-performance, high-cost cutting tools (in both cases with four cutting edges, similar geometrical features and diameters). Three different measures of tool wear are considered in the context of real workshop conditions: on-line power consumption, cutting length and volume of cut material. Finally, decision trees have been selected as the most suitable technique for building a decision-making system for two reasons: these trees show higher accuracy for the prediction of roughness in terms of tool wear and tool type. They also provide useful visual feedback on the information that is extracted from the real data, which can be directly used by the process engineer.},
keywords = {AI in manufacturing systems, cost management, decision support systems, Decision trees, process control, surface roughness, tool condition monitoring},
pubstate = {published},
tppubtype = {article}
}