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Authors: S. M. Karazi and D. Brabazon

Affiliation: School of Mechanical and Manufacturing Engineering and Dublin City University, Ireland

ISBN: 978-989-8425-84-3

Keyword(s): Pulsed Nd:YVO4 laser, ANN, Factorial DoE, Predictive models, Channel dimensions, Polycarbonate.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: This paper presents the development of Artificial Neural Network (ANN) models for the prediction of laser machined internal micro-channels’ dimensions and production costs. In this work, a pulsed Nd:YVO4 laser was used for machining micro-channels in polycarbonate material. Six ANN multi-layered, feed-forward, back-propagation models are presented which were developed on three different training data sets. The analysed data was obtained from a 33 factorial design of experiments (DoE). The controlled parameters were laser power, P; pulse repetition frequency, PRF; and sample translation speed; U. Measured responses were the micro-channel width and the micro-machining operating cost per metre of produced micro-channel. The responses were sufficiently predicted within the set micro-machining parameters limits. Three carefully selected statistical criteria were used for comparing the performance of the ANN predictive models. The comparison showed that model which had the largest amount of training data provided the highest degree of predictability. However, in cases where only a limited amount of ANN training data was available, then training data taken from a Face Centred Cubic (FCC) model design provided the highest level of predictability compared with the other examined training data sets. (More)

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Paper citation in several formats:
M. Karazi, S. and Brabazon, D. (2011). EVALUATION OF THE EFFECT OF ND:YVO4 LASER PARAMETERS ON INTERNAL MICRO-CHANNEL FABRICATION IN POLYCARBONATE.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 254-259. DOI: 10.5220/0003683202540259

@conference{ncta11,
author={S. M. Karazi. and D. Brabazon.},
title={EVALUATION OF THE EFFECT OF ND:YVO4 LASER PARAMETERS ON INTERNAL MICRO-CHANNEL FABRICATION IN POLYCARBONATE},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={254-259},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003683202540259},
isbn={978-989-8425-84-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - EVALUATION OF THE EFFECT OF ND:YVO4 LASER PARAMETERS ON INTERNAL MICRO-CHANNEL FABRICATION IN POLYCARBONATE
SN - 978-989-8425-84-3
AU - M. Karazi, S.
AU - Brabazon, D.
PY - 2011
SP - 254
EP - 259
DO - 10.5220/0003683202540259

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