OPTIMIZATION OF MACHINING PARAMETERS BASED ON ADAPTIVE QUANTUM PARTICLE SWARM NETWORKS (MT)
For improving the machining quality and wear-resistance of intelligent numerically-controlled（NC） machine technology, and decreasing the production cost, an adaptive quantum particle swarm optimization method for machining parameters was proposed. Particle swarm optimization（PSO） method and improved...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2023-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.05.015 |
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| _version_ | 1841534143398150144 |
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| author | HAN HuiHui FU Hui |
| author_facet | HAN HuiHui FU Hui |
| author_sort | HAN HuiHui |
| collection | DOAJ |
| description | For improving the machining quality and wear-resistance of intelligent numerically-controlled（NC） machine technology, and decreasing the production cost, an adaptive quantum particle swarm optimization method for machining parameters was proposed. Particle swarm optimization（PSO） method and improved Elman network are combined to solve the nonlinear and multi-constraint problems of multi-objective NC cutting parameter optimization. Then, quantum mechanism is introduced into PSO algorithm to adjust the fitness through adaptive inertia weight, and the network training is completed by using adaptive momentum back-propagation method. In the process of network learning, the optimal NC cutting parameters are obtained. A KMC800SU five-axis vertical NC machine tool was used to complete the comparison experiment under Matlab 2021a. Taking the surface roughness as an example, the roughing and finishing machining energy of the workpiece obtained by the proposed method can reach 7.6 μm and 3.5 μm respectively, while the PSO method can only reach 8.6 μm and 3.9 μm separately. The results show that the parameter matching of the proposed method is more reasonable than that of the PSO method, and it can achieve stable and better surface roughness, tool wear and average maximum completion time in less iterations. |
| format | Article |
| id | doaj-art-0f9aaa436e3a4655a7bcee9a99b4656f |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2023-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-0f9aaa436e3a4655a7bcee9a99b4656f2025-01-15T02:44:16ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692023-01-011117112344019708OPTIMIZATION OF MACHINING PARAMETERS BASED ON ADAPTIVE QUANTUM PARTICLE SWARM NETWORKS (MT)HAN HuiHuiFU HuiFor improving the machining quality and wear-resistance of intelligent numerically-controlled（NC） machine technology, and decreasing the production cost, an adaptive quantum particle swarm optimization method for machining parameters was proposed. Particle swarm optimization（PSO） method and improved Elman network are combined to solve the nonlinear and multi-constraint problems of multi-objective NC cutting parameter optimization. Then, quantum mechanism is introduced into PSO algorithm to adjust the fitness through adaptive inertia weight, and the network training is completed by using adaptive momentum back-propagation method. In the process of network learning, the optimal NC cutting parameters are obtained. A KMC800SU five-axis vertical NC machine tool was used to complete the comparison experiment under Matlab 2021a. Taking the surface roughness as an example, the roughing and finishing machining energy of the workpiece obtained by the proposed method can reach 7.6 μm and 3.5 μm respectively, while the PSO method can only reach 8.6 μm and 3.9 μm separately. The results show that the parameter matching of the proposed method is more reasonable than that of the PSO method, and it can achieve stable and better surface roughness, tool wear and average maximum completion time in less iterations.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.05.015CuttingQuantum mechanismInertial weightNeural networkPSO method |
| spellingShingle | HAN HuiHui FU Hui OPTIMIZATION OF MACHINING PARAMETERS BASED ON ADAPTIVE QUANTUM PARTICLE SWARM NETWORKS (MT) Jixie qiangdu Cutting Quantum mechanism Inertial weight Neural network PSO method |
| title | OPTIMIZATION OF MACHINING PARAMETERS BASED ON ADAPTIVE QUANTUM PARTICLE SWARM NETWORKS (MT) |
| title_full | OPTIMIZATION OF MACHINING PARAMETERS BASED ON ADAPTIVE QUANTUM PARTICLE SWARM NETWORKS (MT) |
| title_fullStr | OPTIMIZATION OF MACHINING PARAMETERS BASED ON ADAPTIVE QUANTUM PARTICLE SWARM NETWORKS (MT) |
| title_full_unstemmed | OPTIMIZATION OF MACHINING PARAMETERS BASED ON ADAPTIVE QUANTUM PARTICLE SWARM NETWORKS (MT) |
| title_short | OPTIMIZATION OF MACHINING PARAMETERS BASED ON ADAPTIVE QUANTUM PARTICLE SWARM NETWORKS (MT) |
| title_sort | optimization of machining parameters based on adaptive quantum particle swarm networks mt |
| topic | Cutting Quantum mechanism Inertial weight Neural network PSO method |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.05.015 |
| work_keys_str_mv | AT hanhuihui optimizationofmachiningparametersbasedonadaptivequantumparticleswarmnetworksmt AT fuhui optimizationofmachiningparametersbasedonadaptivequantumparticleswarmnetworksmt |