The prediction of chatter-free machining parameters suffers from inaccuracies in dynamic milling model inputs and simplification in milling process modeling, which may lead to a significant mismatch between the predicted stability boundary of the mathematical model and actual physical experiments. This study proposes a novel stability analysis method for milling operations based on a surrogate model that considers the effects of both uncertainties and variations in model inputs. The uncertainties of inputs are quantified by considering the statistical distribution of both cutting force coefficients and modal parameters, and the variations of modal parameters are identified through operational modal analysis (OMA). Furthermore, the proposed method introduces the statistical Kriging surrogate model of the spectral radius in the model parameter domain to propagate uncertainties to the stability lobe diagram (SLD). The confidence interval of the predicted stability boundary is obtained using the estimated prediction variance of the generated Kriging surrogate model. Finally, a mathematical measurement of SLD quality is presented, based on the similarities both in shape and position between the predicted and experimental stability boundaries. The cutting experimental verification and numerical analysis indicated that the robustness and accuracy of the SLD are considerably improved compared to the state-of-the-art methods. Thus, the proposed method holds significant promise for practical engineering applications in controlling milling stability on machining equipment such as CNC tools and industrial robots.

中文翻译：

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基于代理模型预测铣削稳定性凸轮的不确定性量化和动力学特性辨识


无颤振加工参数的预测存在动态铣削模型输入不准确和铣削过程建模简化的问题，这可能导致数学模型的预测稳定性边界与实际物理实验之间存在显着不匹配。本研究提出了一种基于代理模型的铣削操作的新型稳定性分析方法，该方法考虑了模型输入中的不确定性和变化的影响。通过考虑切削力系数和模态参数的统计分布来量化输入的不确定性，并通过工作模态分析 （OMA） 确定模态参数的变化。此外，该方法在模型参数域中引入了谱半径的统计 Kriging 代理模型，以将不确定性传播到稳定性波瓣图 （SLD）。预测稳定性边界的置信区间是使用生成的 Kriging 代理模型的估计预测方差获得的。最后，根据预测和实验稳定性边界之间形状和位置的相似性，提出了 SLD 质量的数学测量。切割实验验证和数值分析表明，与最先进的方法相比，SLD 的稳健性和准确性得到了显著提高。因此，所提出的方法在控制 CNC 工具和工业机器人等加工设备的铣削稳定性方面具有重要的工程应用前景。