Application of multi-stage Monte Carlo method for solving machining optimization problems
Enhancing the overall machining performance implies optimization of machining processes, i.e. determination of optimal machining parameters combination. Optimization of machining processes is an active field of research where different optimization methods are being used to determine an optimal comb...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Growing Science
2014-08-01
|
| Series: | International Journal of Industrial Engineering Computations |
| Subjects: | |
| Online Access: | http://www.growingscience.com/ijiec/Vol5/IJIEC_2014_21.pdf |
| _version_ | 1818330948304896000 |
|---|---|
| author | Miloš Madić Marko Kovačević Miroslav Radovanović |
| author_facet | Miloš Madić Marko Kovačević Miroslav Radovanović |
| author_sort | Miloš Madić |
| collection | DOAJ |
| description | Enhancing the overall machining performance implies optimization of machining processes, i.e. determination of optimal machining parameters combination. Optimization of machining processes is an active field of research where different optimization methods are being used to determine an optimal combination of different machining parameters. In this paper, multi-stage Monte Carlo (MC) method was employed to determine optimal combinations of machining parameters for six machining processes, i.e. drilling, turning, turn-milling, abrasive waterjet machining, electrochemical discharge machining and electrochemical micromachining. Optimization solutions obtained by using multi-stage MC method were compared with the optimization solutions of past researchers obtained by using meta-heuristic optimization methods, e.g. genetic algorithm, simulated annealing algorithm, artificial bee colony algorithm and teaching learning based optimization algorithm. The obtained results prove the applicability and suitability of the multi-stage MC method for solving machining optimization problems with up to four independent variables. Specific features, merits and drawbacks of the MC method were also discussed. |
| first_indexed | 2024-12-13T13:12:04Z |
| format | Article |
| id | doaj.art-c33d9ad047a149779b8f030cb880bb29 |
| institution | Directory Open Access Journal |
| issn | 1923-2926 1923-2934 |
| language | English |
| last_indexed | 2024-12-13T13:12:04Z |
| publishDate | 2014-08-01 |
| publisher | Growing Science |
| record_format | Article |
| series | International Journal of Industrial Engineering Computations |
| spelling | doaj.art-c33d9ad047a149779b8f030cb880bb292022-12-21T23:44:40ZengGrowing ScienceInternational Journal of Industrial Engineering Computations1923-29261923-29342014-08-015464765910.5267/j.ijiec.2014.7.002Application of multi-stage Monte Carlo method for solving machining optimization problemsMiloš MadićMarko KovačevićMiroslav RadovanovićEnhancing the overall machining performance implies optimization of machining processes, i.e. determination of optimal machining parameters combination. Optimization of machining processes is an active field of research where different optimization methods are being used to determine an optimal combination of different machining parameters. In this paper, multi-stage Monte Carlo (MC) method was employed to determine optimal combinations of machining parameters for six machining processes, i.e. drilling, turning, turn-milling, abrasive waterjet machining, electrochemical discharge machining and electrochemical micromachining. Optimization solutions obtained by using multi-stage MC method were compared with the optimization solutions of past researchers obtained by using meta-heuristic optimization methods, e.g. genetic algorithm, simulated annealing algorithm, artificial bee colony algorithm and teaching learning based optimization algorithm. The obtained results prove the applicability and suitability of the multi-stage MC method for solving machining optimization problems with up to four independent variables. Specific features, merits and drawbacks of the MC method were also discussed.http://www.growingscience.com/ijiec/Vol5/IJIEC_2014_21.pdfMonte Carlo methodMulti-stageMachiningOptimizationMeta-heuristics |
| spellingShingle | Miloš Madić Marko Kovačević Miroslav Radovanović Application of multi-stage Monte Carlo method for solving machining optimization problems International Journal of Industrial Engineering Computations Monte Carlo method Multi-stage Machining Optimization Meta-heuristics |
| title | Application of multi-stage Monte Carlo method for solving machining optimization problems |
| title_full | Application of multi-stage Monte Carlo method for solving machining optimization problems |
| title_fullStr | Application of multi-stage Monte Carlo method for solving machining optimization problems |
| title_full_unstemmed | Application of multi-stage Monte Carlo method for solving machining optimization problems |
| title_short | Application of multi-stage Monte Carlo method for solving machining optimization problems |
| title_sort | application of multi stage monte carlo method for solving machining optimization problems |
| topic | Monte Carlo method Multi-stage Machining Optimization Meta-heuristics |
| url | http://www.growingscience.com/ijiec/Vol5/IJIEC_2014_21.pdf |
| work_keys_str_mv | AT milosmadic applicationofmultistagemontecarlomethodforsolvingmachiningoptimizationproblems AT markokovacevic applicationofmultistagemontecarlomethodforsolvingmachiningoptimizationproblems AT miroslavradovanovic applicationofmultistagemontecarlomethodforsolvingmachiningoptimizationproblems |