Integration of simulated annealing and genetic algorithm to estimate optimal solutions for minimising surface roughness in end milling Ti-6AL-4V
In this study, simulated annealing (SA) and genetic algorithm (GA) soft computing techniques are integrated to search for a set of optimal cutting conditions value that leads to the minimum value of machining performance. Two integration systems are proposed; integrated SA-GA-type1 and integrated SA...
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| Main Authors: | , , |
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| Format: | Article |
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Taylor & Francis
2011
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/29210/ http://dx.doi.org/10.1080/0951192X.2011.566629 |
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| Summary: | In this study, simulated annealing (SA) and genetic algorithm (GA) soft computing techniques are integrated to search for a set of optimal cutting conditions value that leads to the minimum value of machining performance. Two integration systems are proposed; integrated SA-GA-type1 and integrated SA-GA-type2. The considered machining performance is surface roughness (R a) in end milling. The results of this study showed that both of the proposed integration systems managed to estimate the optimal cutting conditions, leading to the minimum value of machining performance when compared to the result of real experimental data. The proposed integration systems have also reduced the number of iteration in searching for the optimal solution compared to the conventional GA and conventional SA, respectively. In other words, the time for searching the optimal solution can be made faster by using the integrated SA-GA. |
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