Modelling Laser Milling of Microcavities for the Manufacturing of DES with Ensembles
Autor: | Joaquim Ciurana, Jesús Maudes, Daniel Teixidor, Pedro Santos |
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Přispěvatelé: | Ministerio de Ciencia e Innovación (Espanya) |
Rok vydání: | 2014 |
Předmět: |
Article Subject
Computer science Lasers -- Industrial applications Machine learning computer.software_genre k-nearest neighbors algorithm law.invention law Linear regression Range (statistics) Informática Artificial neural network business.industry lcsh:Mathematics Applied Mathematics Làsers -- Aplicacions industrials Process (computing) lcsh:QA1-939 Laser Regression Support vector machine Artificial intelligence business computer Algorithm |
Zdroj: | © Journal of Applied Mathematics, 2014, vol. 2014, p. 439091 Articles publicats (D-EMCI) DUGiDocs – Universitat de Girona instname Journal of Applied Mathematics, Vol 2014 (2014) J. Appl. Math. Recercat. Dipósit de la Recerca de Catalunya Repositorio Institucional de la Universidad de Burgos (RIUBU) |
ISSN: | 1687-0042 1110-757X |
Popis: | 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. This work was partially funded through Grants fromthe IREBID Project (FP7-PEOPLE-2009-IRSES- 247476) of the European Commission and Projects TIN2011- 24046 and TECNIPLAD (DPI2009-09852) of the Spanish Ministry of Economy and Competitiveness |
Databáze: | OpenAIRE |
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