Path optimization using genetic algorithm in laser scanning system
Laser marking is superior in quality and flexibility to conventional marking techniques such as ink stamping marking. The beam deflected scanning has been commonly applied in the industries. In this paper, the genetic algorithm (GA) has been proposed to optimize the scanning sequence, thus shortenin...
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my.uniten.dspace-297192023-12-28T15:41:47Z Path optimization using genetic algorithm in laser scanning system Chen C.P. Koh S.P. Aris I.B. Albert F.Y.C. Tiong S.K. 25824552100 22951210700 6603306751 56572305600 15128307800 Diesel engines Genetic algorithms Information technology Laser applications Lasers Convergent speeds Cross overs Dynamic variables Laser markings Number representations Path optimizations Real-coded genetic algorithms Scanning paths Scanning systems Sequencing problems Simulation results Two points Scanning Laser marking is superior in quality and flexibility to conventional marking techniques such as ink stamping marking. The beam deflected scanning has been commonly applied in the industries. In this paper, the genetic algorithm (GA) has been proposed to optimize the scanning sequence, thus shortening the required laser scanning path. The GA would base on the real-number representation, namely Real-Coded Genetic Algorithm (RCGA). It employs the Dynamic Variable Length Two Point Crossover (DVL-2PC). The simulation results indicating that the proposed algorithm has good convergent speed and manage to solve the sequencing problem efficiently. � 2008 IEEE. Final 2023-12-28T07:41:47Z 2023-12-28T07:41:47Z 2008 Conference paper 10.1109/ITSIM.2008.4632037 2-s2.0-57349152411 https://www.scopus.com/inward/record.uri?eid=2-s2.0-57349152411&doi=10.1109%2fITSIM.2008.4632037&partnerID=40&md5=6a4dd243cc64e31b1b9be89e43abf500 https://irepository.uniten.edu.my/handle/123456789/29719 4 4632037 All Open Access; Green Open Access Scopus |
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Diesel engines Genetic algorithms Information technology Laser applications Lasers Convergent speeds Cross overs Dynamic variables Laser markings Number representations Path optimizations Real-coded genetic algorithms Scanning paths Scanning systems Sequencing problems Simulation results Two points Scanning |
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Diesel engines Genetic algorithms Information technology Laser applications Lasers Convergent speeds Cross overs Dynamic variables Laser markings Number representations Path optimizations Real-coded genetic algorithms Scanning paths Scanning systems Sequencing problems Simulation results Two points Scanning Chen C.P. Koh S.P. Aris I.B. Albert F.Y.C. Tiong S.K. Path optimization using genetic algorithm in laser scanning system |
| description |
Laser marking is superior in quality and flexibility to conventional marking techniques such as ink stamping marking. The beam deflected scanning has been commonly applied in the industries. In this paper, the genetic algorithm (GA) has been proposed to optimize the scanning sequence, thus shortening the required laser scanning path. The GA would base on the real-number representation, namely Real-Coded Genetic Algorithm (RCGA). It employs the Dynamic Variable Length Two Point Crossover (DVL-2PC). The simulation results indicating that the proposed algorithm has good convergent speed and manage to solve the sequencing problem efficiently. � 2008 IEEE. |
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25824552100 |
| author_facet |
25824552100 Chen C.P. Koh S.P. Aris I.B. Albert F.Y.C. Tiong S.K. |
| format |
Conference paper |
| author |
Chen C.P. Koh S.P. Aris I.B. Albert F.Y.C. Tiong S.K. |
| author_sort |
Chen C.P. |
| title |
Path optimization using genetic algorithm in laser scanning system |
| title_short |
Path optimization using genetic algorithm in laser scanning system |
| title_full |
Path optimization using genetic algorithm in laser scanning system |
| title_fullStr |
Path optimization using genetic algorithm in laser scanning system |
| title_full_unstemmed |
Path optimization using genetic algorithm in laser scanning system |
| title_sort |
path optimization using genetic algorithm in laser scanning system |
| publishDate |
2023 |
| _version_ |
1806424443931590656 |
| score |
13.222552 |