Chatter in machining results in poor workpiece surface quality and short tool life. An accurate and reliable chatter detection method is needed before its complete development. This paper applies a novel p-leader multifractal formalism for chatter detection in milling processes. This novel formalism can discover internal singularities rising on unstable signals due to chatter without prior knowledge of the natural frequencies of the machining system. The p-leader multifractal features are selected by using a multivariate filter method for feature selection, and verified by both numerical simulations and experimental studies with detailed parameter selection discussions when applying this formalism. The proposed method is assessed in terms of their dynamic monitoring abilities and classification accuracies under wide cutting conditions. The results show that the multifractal features can successfully detect chatter with high accuracies and short computation time. For further verification, the proposed method is compared with two commonly-used methods, which indicates that the proposed method gives better classification accuracies, especially when identifying unstable tests.

加工中的振颤会导致较差的工件表面质量和较短的刀具寿命。在其全面开发之前，需要一种准确可靠的颤动检测方法。本文将一种新颖的p -leader多重分形形式应用于铣削过程中的颤动检测。这种新颖的形式主义可以发现由于颤动而在不稳定信号上产生的内部奇异点，而无需事先了解加工系统的固有频率。该p-leader多重分形特征是通过多元过滤方法进行特征选择的，并通过数值模拟和实验研究进行了验证，并在应用这种形式主义时进行了详细的参数选择讨论。在广泛的切割条件下，根据其动态监控能力和分类精度对所提出的方法进行评估。结果表明，多重分形特征可以准确，快速地检测出颤动。为了进一步验证，将该方法与两种常用方法进行了比较，这表明该方法具有更好的分类准确性，尤其是在识别不稳定测试时。