Zhang Yiwei, Yan Mengke, Lai Yushu, Wang Guixing, Yang Yifan
Chongqing Engineering Research Center for Advanced Intelligent Manufacturing Technology, Chongqing Three Gorges University, Chongqing 404000, China.
Chongqing Engineering Technology Research Center for Light Alloy and Processing, Chongqing Three Gorges University, Chongqing 404000, China.
Materials (Basel). 2024 Nov 28;17(23):5844. doi: 10.3390/ma17235844.
The milling force plays a pivotal role in CFRP milling. Modeling of the milling force is helpful to explore the changing law, optimize the processing parameters, and then reduce the appearance of defects. However, most of the existing models ignore the effect of the bottom edge. In this paper, the prediction of milling force in CFRP milling processes is taken as the research object. By analyzing the milling mechanism and considering the end milling cutter's bottom cutting edge, the prediction model of milling force was established. Based on the experimental data and simulation data of milling force, the milling force coefficient was obtained by inverse calculation. Subsequently, the predicted cutting force was compared with the experimental cutting force, showing a maximum error of 14.5%, which is within a reasonable range, and the correctness of the model was verified. Furthermore, combined with the delamination damage and the milling force prediction model, a multi-objective optimization model of milling parameters was established, and the genetic algorithm was used to solve the model. The unidirectional carbon fiber plate with a fiber direction angle of 45° was selected as the optimization example. The minimum delamination damage was obtained under the cutting conditions of a spindle speed of 4903.1569 r/min, feed rate per tooth of 0.01 mm/z, and an axial depth of cut of 0.5 mm, and the experimental verification was carried out. The feasibility of the genetic algorithm in CFRP milling parameter optimization modeling was also verified.
铣削力在碳纤维增强塑料（CFRP）铣削中起着关键作用。铣削力建模有助于探索其变化规律、优化加工参数，进而减少缺陷的出现。然而，现有的大多数模型都忽略了底刃的影响。本文以CFRP铣削过程中的铣削力预测为研究对象。通过分析铣削机理并考虑立铣刀的底切削刃，建立了铣削力预测模型。基于铣削力的实验数据和仿真数据，通过反算得到了铣削力系数。随后，将预测切削力与实验切削力进行比较，最大误差为14.5%，在合理范围内，验证了模型的正确性。此外，结合分层损伤和铣削力预测模型，建立了铣削参数多目标优化模型，并采用遗传算法求解该模型。选取纤维方向角为45°的单向碳纤维板作为优化实例。在主轴转速为4903.1569 r/min、每齿进给量为0.01 mm/z、轴向切削深度为0.5 mm的切削条件下，获得了最小分层损伤，并进行了实验验证。同时也验证了遗传算法在CFRP铣削参数优化建模中的可行性。
