2014
Santos, Pedro; Teixidor, Daniel; Maudes-Raedo, Jesús; Ciurana, Joaquim
Modelling Laser Milling of Microcavities for the Manufacturing of DES with Ensembles Journal Article
In: Journal of Applied Mathematics, vol. 2014, pp. 15, 2014, ISBN: 1110-757X.
Abstract | Links | BibTeX | Tags: Ensemble methods, Laser milling, Neural networks, Support vector machines
@article{Santos2014,
title = {Modelling Laser Milling of Microcavities for the Manufacturing of DES with Ensembles},
author = {Pedro Santos and Daniel Teixidor and Jesús Maudes-Raedo and Joaquim Ciurana},
url = {https://www.hindawi.com/journals/jam/2014/439091/},
doi = {10.1155/2014/439091},
isbn = {1110-757X},
year = {2014},
date = {2014-04-17},
journal = {Journal of Applied Mathematics},
volume = {2014},
pages = {15},
abstract = {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.},
keywords = {Ensemble methods, Laser milling, Neural networks, Support vector machines},
pubstate = {published},
tppubtype = {article}
}
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.