薄壁零件铣削时变动力学参数预测与实验验证
收稿日期: 2024-11-15
修回日期: 2024-12-03
录用日期: 2025-03-16
网络出版日期: 2025-03-31
基金资助
国家自然科学基金(51765047);江西省自然科学基金重点项目(20232ACB204019);广西科技重大专项(Guike AA23023027-3)
Prediction and experimental verification of time-varying dynamic parameters for milling thin-walled workpieces
Received date: 2024-11-15
Revised date: 2024-12-03
Accepted date: 2025-03-16
Online published: 2025-03-31
Supported by
National Natural Science Foundation of China(51765047);Key Projects of Jiangxi Provincial Natural Science Foundation(20232ACB204019);Guangxi Science and Technology Major Program(Guike AA23023027-3)
薄壁零件铣削过程动力学参数的时变特性会影响铣削稳定性区域的预报。为了准确高效地获取薄壁零件铣削时的时变动力学参数，在建立考虑切削厚度动态变化动态切削力预测模型的基础上，将铣削中工件受动态力的振动等效为薄板受垂直中面激励力引起的振动求解问题，基于假设振型法计算工件初始动力学参数。考虑铣削过程材料去除和进给位置变化，根据结构动力修改方法快速获得时变的工件系统动力学参数。与有限元仿真相比，所提方法由于不需要重复建模从而计算效率提高90%以上。与实验测试方法相比，所提方法仅需一次初始测量实验，无需停机测量工件加工过程的动力学参数。实验结果显示，材料去除过程中零件的频响函数曲线和固有频率会发生显著变化，频率最大变化量达到25%，所提方法的预测结果与实验测量结果相比最大误差为4.816%。
娄维达 , 秦国华 , 万敏 , 朱智翔 . 薄壁零件铣削时变动力学参数预测与实验验证[J]. 航空学报, 2025 , 46(14) : 431540 -431540 . DOI: 10.7527/S1000-6893.2025.31540
The time-varying characteristics of dynamic parameters during the thin-walled part milling process significantly affect the prediction of milling stability regions. To accurately and efficiently acquire these time-varying dynamic parameters， a dynamic cutting force prediction model considering the dynamic variation of cutting thickness is established. The vibration of the workpiece under dynamic cutting forces is equivalently modeled as a thin plate subjected to vertical in-plane excitation forces， and the initial dynamic parameters of the workpiece are calculated using the assumed mode method. Material removal and feed position variations during milling are incorporated， enabling rapid acquisition of time-varying system dynamic parameters through structural dynamic modification methods. Compared with finite element simulations， the proposed method improves computational efficiency by over 90% as it eliminates the need for repetitive modeling. Unlike experimental testing approaches， this method requires only one initial measurement experiment without interrupting the in-process parameter measurements. Experimental results demonstrate significant changes in frequency response function curves and natural frequencies during material removal， with maximum frequency variations reaching 25%. The maximum prediction error between the proposed method and experimental measurements is 4.816%.
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