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Swarm Intelligent Selection and Optimization of Machining System Parameters for Microchannel Fabrication in Medical Devices

Current technology trends in medical device industry calls for fabrication of massive arrays of microfeatures such as microchannels on to nonsilicon material substrates with high accuracy, superior precision, and high throughput. Microchannels are typical features used in medical devices for medication dosing into the human body, analyzing DNA arrays or cell cultures. In this study, the capabilities of machining systems for micro-end milling have been evaluated by conducting experiments, regression modeling, and response surface methodology. In machining experiments by using micromilling, arrays of microchannels are fabricated on aluminium and titanium plates, and the feature size and accuracy (width and depth) and surface roughness are measured. Multicriteria decision making for material and process parameters selection for desired accuracy is investigated by using particle swarm optimization (PSO) method, which is an evolutionary computation method inspired by genetic algorithms (GA). Appropriate regression models are utilized within the PSO and optimum selection of micromilling parameters; microchannel feature accuracy and surface roughness are performed. An analysis for optimal micromachining parameters in decision variable space is also conducted. This study demonstrates the advantages of evolutionary computing algorithms in micromilling decision making and process optimization investigations and can be expanded to other applications

© Materials and Manufacturing Processes, 2011, vol. 26, núm. 3, p. 403-414

Author: Vázquez Lepe, Elisa
Ciurana, Quim de
Rodríguez González, Ciro Ángel
Thepsonthi, Thanongsak
Özel, Tuǧrul
Date: 2011
Abstract: Current technology trends in medical device industry calls for fabrication of massive arrays of microfeatures such as microchannels on to nonsilicon material substrates with high accuracy, superior precision, and high throughput. Microchannels are typical features used in medical devices for medication dosing into the human body, analyzing DNA arrays or cell cultures. In this study, the capabilities of machining systems for micro-end milling have been evaluated by conducting experiments, regression modeling, and response surface methodology. In machining experiments by using micromilling, arrays of microchannels are fabricated on aluminium and titanium plates, and the feature size and accuracy (width and depth) and surface roughness are measured. Multicriteria decision making for material and process parameters selection for desired accuracy is investigated by using particle swarm optimization (PSO) method, which is an evolutionary computation method inspired by genetic algorithms (GA). Appropriate regression models are utilized within the PSO and optimum selection of micromilling parameters; microchannel feature accuracy and surface roughness are performed. An analysis for optimal micromachining parameters in decision variable space is also conducted. This study demonstrates the advantages of evolutionary computing algorithms in micromilling decision making and process optimization investigations and can be expanded to other applications
Format: application/pdf
ISSN: 1042-6914 (versió paper)
1532-2475 (versió electrònica)
Document access: http://hdl.handle.net/10256/7150
Language: eng
Collection: Reproducció digital del document publicat a: http://dx.doi.org/10.1080/10426914.2010.520792
Articles publicats (D-EMCI)
info:eu-repo/grantAgreement/EC/FP7/247476
Is part of: © Materials and Manufacturing Processes, 2011, vol. 26, núm. 3, p. 403-414
Rights: Tots els drets reservats
Subject: Medicina -- Aparells i instruments
Medical instruments and apparatus
Micromecànica
Micromechanics
Title: Swarm Intelligent Selection and Optimization of Machining System Parameters for Microchannel Fabrication in Medical Devices
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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