A new approach for the inverse identification of structural dynamics and process parameters in milling operations is presented. An algorithm is fed with process information and the stability state of recorded cuts. By comparing stability model predictions with experimental data, the underlying relationships and governing parameters are estimated with the help of Artificial Neural Networks. The capabilities of the approach are demonstrated using a simulative example and two experimental studies. The algorithm shows to be capable of approximating unknown relationships such as spindle speed dependent dynamics, which in turn allows for accurate stability predictions without the need for extensive dedicated measurements.

提出了一种在铣削加工中逆识别结构动力学和工艺参数的新方法。向算法提供过程信息和已记录切割的稳定性状态。通过将稳定性模型预测与实验数据进行比较，借助人工神经网络可以估算出潜在的关系和控制参数。通过一个模拟示例和两次实验研究证明了该方法的功能。该算法显示出能够近似未知关系（例如依赖于主轴速度的动态特性）的能力，从而可以进行精确的稳定性预测，而无需进行大量的专门测量。