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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 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.
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 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.
OPTIMIZATION OF MACHINING PARAMETERS BASED ON ADAPTIVE QUANTUM PARTICLE SWARM NETWORKS (MT)
HAN HuiHui (author) / FU Hui (author)
2023
Article (Journal)
Electronic Resource
Unknown
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