In the coming decades, robotized mobile machining equipment (RMME) is possible to evolve as a new branch of machine tools due to its exceptional flexibility. The frequency response function (FRF) serves as a theoretical foundation in controlling the vibration deformations that significantly limit the material removal efficiency of RMME. Model updating, aimed at minimizing errors between the theoretical model and the physical prototype, is essential to predict the FRF accurately. However, updating the dynamic model of RMME, characterized by non-mechanical boundary conditions, complex lightweight components, and low-stiffness structures, presents difficulties in computational efficiency and updating posture-dependent parameters. To solve these issues, the prediction error generation mechanism is first analyzed to confirm the error types that need to be eliminated in model updating. A two-stage model updating method that can separately update the robot structure and boundary-related parameters is proposed to rapidly update the dynamic model under various machining tasks. The interface reduction technique that can decrease the model order is introduced to reduce the computational consumption, and an approach to fast update such interface reduction substructures is put forward to avoid the reiterative model reduction during updating. An updating method for posture-dependent parameters based on multi-objective optimization is designed to control the multiple solution issues by generating many feasible solutions, ensuring the prediction effects for non-updated postures. The experimental results indicate that updating structural parameters in stage I and adsorption surface parameters in stage II results in mean error reduction percentages of 58.79 % and 48.30 %, respectively. Additionally, the natural frequencies and the mode shapes can also be predicted by the updated model. Comparative analysis with various controlled groups confirms the advantages of utilizing posture-dependent parameters in prediction accuracy and adopting the two-stage model updating method in efficiency. The proposed method can also be applied to other RMMEs.

中文翻译：

---

一种用于机器人移动加工设备 FRF 预测的两阶段动态模型更新方法


在未来几十年里，机器人移动加工设备 （RMME） 由于其卓越的灵活性，有可能发展成为机床的一个新分支。频率响应函数 （FRF） 是控制振动变形的理论基础，振动变形会显着限制 RMME 的材料去除效率。模型更新旨在最大限度地减少理论模型和物理原型之间的误差，对于准确预测 FRF 至关重要。然而，更新以非机械边界条件、复杂轻量化组件和低刚度结构为特征的 RMME 动力学模型，在计算效率和更新姿态相关参数方面存在困难。为了解决这些问题，首先分析了预测误差的产生机制，以确认模型更新中需要消除的错误类型。提出了一种能够分别更新机器人结构和边界相关参数的两阶段模型更新方法，以快速更新各种加工任务下的动态模型。为了降低计算量消耗，引入了可以降低模型阶数的界面缩减技术，并提出了一种快速更新此类界面缩减子结构的方法，以避免更新过程中的重复模型缩减。该文提出一种基于多目标优化的姿态相关参数更新方法，通过生成许多可行的解来控制多个解问题，保证对未更新姿态的预测效果。实验结果表明，更新阶段 I 的结构参数和阶段 II 的吸附表面参数导致平均误差降低百分比分别为 58.79 % 和 48.30 %。 此外，更新后的模型还可以预测固有频率和振型。与各种控制组的比较分析证实了利用姿态依赖参数提高预测精度和采用两阶段模型更新方法提高效率的优势。所提出的方法也可以应用于其他 RMME。