Jiang Li, Jiutao Zhao, Qinhui Liu*, Laizheng Zhu, Jinyi Guo, Weijiu Zhang
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 223-244, 2023, DOI:10.32604/cmc.2023.042429
Abstract Cutting parameters have a significant impact on the machining effect. In order to reduce the machining time and improve the machining quality, this paper proposes an optimization algorithm based on Bp neural network-Improved Multi-Objective Particle Swarm (Bp-DWMOPSO). Firstly, this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm. Secondly, the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established. Finally, the Bp-DWMOPSO algorithm is designed based on the established models. In order to verify the effectiveness of the algorithm, this paper obtains the required data through More >
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