To provide a model for real-time prediction of cutting forces, vibrations and optimal energy consumption (EC) model, a new hybrid algorithm based on Back-Propagation Neural Network and Multi-Objective Particle Swarm Optimization (BPNN-MOPSO) was developed to determine the optimal cutting parameters with minimal total energy consumption. High-speed milling experiments were conducted to confirm the proposed online monitoring and optimal model’s accuracy and availability. The proposed optimal method, based on the proposed improvement model, can reduce EC by 10.49% compared to the empirical option method. The feasibility and effectiveness of the proposed model have been verified through experimental processes.

中文翻译：

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奇异振动在高速铣削中的最低能耗应用

为了提供实时预测切削力、振动和最佳能耗 (EC) 模型的模型，开发了一种基于反向传播神经网络和多目标粒子群优化 (BPNN-MOPSO) 的新混合算法来确定以最小的总能耗获得最佳切割参数。进行了高速铣削实验，以确认所提出的在线监测和优化模型的准确性和可用性。所提出的最优方法，基于提出的改进模型，与经验选择方法相比，EC 可以降低 10.49%。通过实验过程验证了所提出模型的可行性和有效性。