NUMERICAL CONTROL MILLING PARAMETER OPTIMIZATION ON THE BASIS OF IMPROVED GENETIC ALGORITHM
Numerical control machining is widely used in traditional manufacturing industry and national defense strategic industry, and optimization of milling parameters is closely related to processing efficiency, quality and cost. Firstly, a constrained multi-objective optimization function is constructed...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2022-01-01
|
| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2022.03.016 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841536037721997312 |
|---|---|
| author | YAN ShengLi FU Hui LI Hao |
| author_facet | YAN ShengLi FU Hui LI Hao |
| author_sort | YAN ShengLi |
| collection | DOAJ |
| description | Numerical control machining is widely used in traditional manufacturing industry and national defense strategic industry, and optimization of milling parameters is closely related to processing efficiency, quality and cost. Firstly, a constrained multi-objective optimization function is constructed as a parameter optimization model. Then, the ENDE-NSGA-II method is adopted to complete the parameter optimization. By adoption of the NDX crossover algorithm, CA sorting method and DE strategy can increase the search interval, ensure the population diversification distribution, and improve the convergence rate. The DMU125 P five-axis CNC machine tool combined with Matlab 2020 was used to complete the comparison experiment of processing parameters, quality and system steady-state. Experimental results show that in comparison with the traditional empirical processing method, the processing parameters optimized by the ENDE-NSGA-II method can improve the surface roughness and the processing quality of the workpiece, under the premise of ensuring the processing efficiency. And increase the wear resistance of cutting tools, improve economic benefits. In addition, it allows the system to reach steady state more quickly. |
| format | Article |
| id | doaj-art-99c5153a08544f2492fa01d907da759a |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2022-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-99c5153a08544f2492fa01d907da759a2025-01-15T02:24:21ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692022-01-014462062629913249NUMERICAL CONTROL MILLING PARAMETER OPTIMIZATION ON THE BASIS OF IMPROVED GENETIC ALGORITHMYAN ShengLiFU HuiLI HaoNumerical control machining is widely used in traditional manufacturing industry and national defense strategic industry, and optimization of milling parameters is closely related to processing efficiency, quality and cost. Firstly, a constrained multi-objective optimization function is constructed as a parameter optimization model. Then, the ENDE-NSGA-II method is adopted to complete the parameter optimization. By adoption of the NDX crossover algorithm, CA sorting method and DE strategy can increase the search interval, ensure the population diversification distribution, and improve the convergence rate. The DMU125 P five-axis CNC machine tool combined with Matlab 2020 was used to complete the comparison experiment of processing parameters, quality and system steady-state. Experimental results show that in comparison with the traditional empirical processing method, the processing parameters optimized by the ENDE-NSGA-II method can improve the surface roughness and the processing quality of the workpiece, under the premise of ensuring the processing efficiency. And increase the wear resistance of cutting tools, improve economic benefits. In addition, it allows the system to reach steady state more quickly.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2022.03.016Multi-objectiveNDX crossoverCA sortingENDE-NSGA-IINumerical control machining |
| spellingShingle | YAN ShengLi FU Hui LI Hao NUMERICAL CONTROL MILLING PARAMETER OPTIMIZATION ON THE BASIS OF IMPROVED GENETIC ALGORITHM Jixie qiangdu Multi-objective NDX crossover CA sorting ENDE-NSGA-II Numerical control machining |
| title | NUMERICAL CONTROL MILLING PARAMETER OPTIMIZATION ON THE BASIS OF IMPROVED GENETIC ALGORITHM |
| title_full | NUMERICAL CONTROL MILLING PARAMETER OPTIMIZATION ON THE BASIS OF IMPROVED GENETIC ALGORITHM |
| title_fullStr | NUMERICAL CONTROL MILLING PARAMETER OPTIMIZATION ON THE BASIS OF IMPROVED GENETIC ALGORITHM |
| title_full_unstemmed | NUMERICAL CONTROL MILLING PARAMETER OPTIMIZATION ON THE BASIS OF IMPROVED GENETIC ALGORITHM |
| title_short | NUMERICAL CONTROL MILLING PARAMETER OPTIMIZATION ON THE BASIS OF IMPROVED GENETIC ALGORITHM |
| title_sort | numerical control milling parameter optimization on the basis of improved genetic algorithm |
| topic | Multi-objective NDX crossover CA sorting ENDE-NSGA-II Numerical control machining |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2022.03.016 |
| work_keys_str_mv | AT yanshengli numericalcontrolmillingparameteroptimizationonthebasisofimprovedgeneticalgorithm AT fuhui numericalcontrolmillingparameteroptimizationonthebasisofimprovedgeneticalgorithm AT lihao numericalcontrolmillingparameteroptimizationonthebasisofimprovedgeneticalgorithm |