Ma Junjin, Yan Xinhong, Li Yunfei, Li Haoming, Li Yujie, Pang Xiaoyan
School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454000, China.
College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China.
Micromachines (Basel). 2022 Sep 22;13(10):1581. doi: 10.3390/mi13101581.
The process parameters chosen for high-performance machining in the milling of a thin-walled workpiece are determined by a stability prediction model, which needs accurate modal parameters of the machining system. However, the in-process modal parameters are different from the offline modal parameters and are difficult to precisely obtain due to material removal. To address this problem, an accurate time-dependent autoregressive moving average with an exogenous input (TARMAX) method is proposed for the identification of the modal parameters in the milling of a thin-walled workpiece. In this process, a TARMAX model considering external force excitation is constructed to characterize the actual condition in the milling of a thin-walled workpiece. Then, recursive method and sliding window recursive method are used to identify TARMAX model parameters under time-varying cutting conditions. Subsequently, a three-degree of freedom (3-DOF) time-varying structure numerical model under theoretical milling forces and white-noise excitation is established, and the computational results show that the predicted natural frequencies using the proposed method are in close agreement with the simulated values. Finally, several experiments are designed and carried out to validate the effectiveness of the proposed method. The experimental results show that the predicted accuracy of the proposed method using actual cutting forces is 95.68%. Good agreement has been drawn in the numerical simulation and machining experiments. Our further research objectives will focus on the prediction of the damping ratios, modal stiffness, and modal mass.
用于薄壁工件铣削中高性能加工的工艺参数由稳定性预测模型确定,该模型需要加工系统的精确模态参数。然而,加工过程中的模态参数与离线模态参数不同,并且由于材料去除而难以精确获取。为了解决这个问题,提出了一种精确的带外部输入的时变自回归滑动平均(TARMAX)方法,用于识别薄壁工件铣削中的模态参数。在此过程中,构建了一个考虑外力激励的TARMAX模型,以表征薄壁工件铣削中的实际情况。然后,使用递归方法和滑动窗口递归方法在时变切削条件下识别TARMAX模型参数。随后,建立了一个在理论铣削力和白噪声激励下的三自由度(3-DOF)时变结构数值模型,计算结果表明,使用所提出方法预测的固有频率与模拟值非常吻合。最后,设计并进行了几个实验,以验证所提出方法的有效性。实验结果表明,使用实际切削力的所提出方法的预测准确率为95.68%。数值模拟和加工实验取得了良好的一致性。我们进一步的研究目标将集中在阻尼比、模态刚度和模态质量的预测上。