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Journal of Applied Mathematics
Volume 2014 (2014), Article ID 439091, 15 pages
Research Article

Modelling Laser Milling of Microcavities for the Manufacturing of DES with Ensembles

1Department of Civil Engineering, Higher Polytechnic School, University of Burgos, Cantabria Avenue, 09006 Burgos, Spain
2Department of Mechanical Engineering and Industrial Construction, University of Girona, Maria Aurelia Capmany 61, 17003 Girona, Spain

Received 28 December 2013; Accepted 11 March 2014; Published 17 April 2014

Academic Editor: Aderemi Oluyinka Adewumi

Copyright © 2014 Pedro Santos et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A set of designed experiments, involving the use of a pulsed Nd:YAG laser system milling 316L Stainless Steel, serve to study the laser-milling process of microcavities in the manufacture of drug-eluting stents (DES). Diameter, depth, and volume error are considered to be optimized as functions of the process parameters, which include laser intensity, pulse frequency, and scanning speed. Two different DES shapes are studied that combine semispheres and cylinders. Process inputs and outputs are defined by considering the process parameters that can be changed under industrial conditions and the industrial requirements of this manufacturing process. In total, 162 different conditions are tested in a process that is modeled with the following state-of-the-art data-mining regression techniques: Support Vector Regression, Ensembles, Artificial Neural Networks, Linear Regression, and Nearest Neighbor Regression. Ensemble regression emerged as the most suitable technique for studying this industrial problem. Specifically, Iterated Bagging ensembles with unpruned model trees outperformed the other methods in the tests. This method can predict the geometrical dimensions of the machined microcavities with relative errors related to the main average value in the range of 3 to 23%, which are considered very accurate predictions, in view of the characteristics of this innovative industrial task.