Abstract Micro milling process aims to manufacture complex micro/meso structures, and the reduction of material removal volume determines the possible decrease of the total energy use, which would put less pressure on the environment. However, the energy consumption of micro milling influenced by tool wear and tool run-out would be augmented and result in the drawback of more energy consumption. In order to reduce the energy consumption of micro milling process, a new analytical energy consumption model and the related optimization of cutting parameters are presented in this paper. Although the influence of tool wear is inevitable, it hasn't been thoroughly concerned in the existing energy consumption models. Therefore, the stochastic tool wear progression, which can be obtained from a probabilistic approach based on the online measured cutting forces, is integrated into the proposed energy model. In addition, the process nonlinearities caused by tool run-out and the trochoidal trajectories of cutting edge are also considered in the model. With the developed prediction model of energy consumption, a hybrid cuckoo search and grey wolf algorithm is used to determine the optimum cutting parameters for minimizing the total energy consumption. The micro milling experiments are performed to validate the accuracy and availability of the proposed energy consumption model and the optimization method. The improved optimization method based on the proposed energy model can reduce the energy consumption by 7.89% compared with the empirical selection.

中文翻译：

---

考虑刀具磨损的能耗及微铣削加工切削参数的优化

摘要 微铣削工艺旨在制造复杂的微/细观结构，材料去除量的减少决定了总能耗的可能降低，从而对环境的压力更小。然而，微铣削受刀具磨损和刀具跳动影响的能耗会增加，从而导致能耗增加的缺点。为了降低微铣削过程的能耗，本文提出了一种新的分析能耗模型和切削参数的相关优化。虽然刀具磨损的影响是不可避免的，但在现有的能耗模型中并没有得到充分的关注。因此，随机刀具磨损进程，可以从基于在线测量的切削力的概率方法中获得，并集成到所提出的能量模型中。此外，模型中还考虑了刀具跳动和切削刃摆线轨迹引起的过程非线性。借助开发的能耗预测模型，使用混合布谷鸟搜索和灰狼算法来确定使总能耗最小的最佳切割参数。进行微铣削实验以验证所提出的能耗模型和优化方法的准确性和可用性。与经验选择相比，基于所提出的能量模型的改进优化方法可以降低7.89%的能耗。此外，模型中还考虑了刀具跳动和切削刃摆线轨迹引起的过程非线性。借助开发的能耗预测模型，使用混合布谷鸟搜索和灰狼算法来确定使总能耗最小的最佳切割参数。进行微铣削实验以验证所提出的能耗模型和优化方法的准确性和可用性。与经验选择相比，基于所提出的能量模型的改进优化方法可以降低7.89%的能耗。此外，模型中还考虑了刀具跳动和切削刃摆线轨迹引起的过程非线性。借助开发的能耗预测模型，使用混合布谷鸟搜索和灰狼算法来确定使总能耗最小的最佳切割参数。进行微铣削实验以验证所提出的能耗模型和优化方法的准确性和可用性。与经验选择相比，基于所提出的能量模型的改进优化方法可以降低7.89%的能耗。混合布谷鸟搜索和灰狼算法用于确定最佳切割参数，以最大限度地减少总能耗。进行微铣削实验以验证所提出的能耗模型和优化方法的准确性和可用性。与经验选择相比，基于所提出的能量模型的改进优化方法可以降低7.89%的能耗。混合布谷鸟搜索和灰狼算法用于确定最佳切割参数，以最大限度地减少总能耗。进行微铣削实验以验证所提出的能耗模型和优化方法的准确性和可用性。与经验选择相比，基于所提出的能量模型的改进优化方法可以降低7.89%的能耗。