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An automated gravimetric & PWM based fluid dispensing system with GA parameter fine tuning

In this paper we present a gravimetric and Pulse Width Modulation (PWM) based fluid dispensing technique for a maximum dispense batch of 30kg. In addition, a Genetic Algorithm (GA) Parameter Fine Tuning technique is presented as well. Based on the combination of both techniques, the system is able t...

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Main Authors: Sim, E.Y.S., Koh, S.P., Tiong, S.K., Yap, B.K.
Format: Conference Paper
Language:en_US
Published: 2017
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spelling my.uniten.dspace-58382018-01-05T02:08:42Z An automated gravimetric & PWM based fluid dispensing system with GA parameter fine tuning Sim, E.Y.S. Koh, S.P. Tiong, S.K. Yap, B.K. In this paper we present a gravimetric and Pulse Width Modulation (PWM) based fluid dispensing technique for a maximum dispense batch of 30kg. In addition, a Genetic Algorithm (GA) Parameter Fine Tuning technique is presented as well. Based on the combination of both techniques, the system is able to dispense up to 50 samples with an accuracy of +/- 2g with the dispensing speed varies between 50 seconds to 60 seconds for a 5.2kg batch within one dispense valve. The reported dispensing technique is based on PWM technique in the dispensing sequence and GA technique in the parameter fine tuning. This technique could overcome limitations of the volumetric dispensing and manual parameter tuning presently applied in the coatings industry. The fast and accurate system which is modularly built out of individual dispense valve is able to handle different fluids with varying viscosity. The working principles of the system as well as its accuracy results are presented. ©2009 IEEE. 2017-12-08T07:26:37Z 2017-12-08T07:26:37Z 2009 Conference Paper 10.1109/ICARA.2000.4803956 en_US ICARA 2009 - Proceedings of the 4th International Conference on Autonomous Robots and Agents 2009, Article number 4803956, Pages 166-170
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language en_US
description In this paper we present a gravimetric and Pulse Width Modulation (PWM) based fluid dispensing technique for a maximum dispense batch of 30kg. In addition, a Genetic Algorithm (GA) Parameter Fine Tuning technique is presented as well. Based on the combination of both techniques, the system is able to dispense up to 50 samples with an accuracy of +/- 2g with the dispensing speed varies between 50 seconds to 60 seconds for a 5.2kg batch within one dispense valve. The reported dispensing technique is based on PWM technique in the dispensing sequence and GA technique in the parameter fine tuning. This technique could overcome limitations of the volumetric dispensing and manual parameter tuning presently applied in the coatings industry. The fast and accurate system which is modularly built out of individual dispense valve is able to handle different fluids with varying viscosity. The working principles of the system as well as its accuracy results are presented. ©2009 IEEE.
format Conference Paper
author Sim, E.Y.S.
Koh, S.P.
Tiong, S.K.
Yap, B.K.
spellingShingle Sim, E.Y.S.
Koh, S.P.
Tiong, S.K.
Yap, B.K.
An automated gravimetric & PWM based fluid dispensing system with GA parameter fine tuning
author_facet Sim, E.Y.S.
Koh, S.P.
Tiong, S.K.
Yap, B.K.
author_sort Sim, E.Y.S.
title An automated gravimetric & PWM based fluid dispensing system with GA parameter fine tuning
title_short An automated gravimetric & PWM based fluid dispensing system with GA parameter fine tuning
title_full An automated gravimetric & PWM based fluid dispensing system with GA parameter fine tuning
title_fullStr An automated gravimetric & PWM based fluid dispensing system with GA parameter fine tuning
title_full_unstemmed An automated gravimetric & PWM based fluid dispensing system with GA parameter fine tuning
title_sort automated gravimetric & pwm based fluid dispensing system with ga parameter fine tuning
publishDate 2017
_version_ 1644493788267151360
score 13.222552

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