Abstract Complex machine-structure dynamics of a movable stage affects observer-based cutting force estimation. A dynamic compensation approach based on the concept of machine-in-the-loop learning is proposed to enhance the accuracy of cutting force estimation based on a disturbance-observer. Machine dynamics induced estimation errors are pre-compensated by modifying a digital filter representing an inverse disturbance transfer function. The order and parameters of the filter are self-optimized to enhance the estimation accuracy during iterative pre-milling tests with various rotational spindle speeds. The experimental results show that the proposed self-optimized filter achieves accurate wide-band cutting force estimation in milling process.

中文翻译：

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基于扰动观测器的切削力估算机床动力学预补偿

摘要 可移动平台的复杂机器结构动力学影响基于观察器的切削力估计。提出了一种基于机器在环学习概念的动态补偿方法，以提高基于干扰观察器的切削力估计的准确性。通过修改表示逆扰动传递函数的数字滤波器来预补偿机器动力学引起的估计误差。滤波器的阶数和参数是自我优化的，以提高在各种主轴转速下迭代预铣削测试过程中的估计精度。实验结果表明，所提出的自优化滤波器在铣削过程中实现了精确的宽带切削力估计。