Cutting chatter recognition based on spectrum characteristics and extreme gradient boosting

• Published in: International journal of advanced manufacturing technology 2024-04, Vol.131 (12), p.6115-6135
• Main Authors: Liu, Hongqi, Mao, Xinyong, Zhu, Qiuning, Zeng, Shaokun, Li, Bin, He, Songping, Peng, Fangyu, Zhu, Jiaming
• Format: Article
• Language: English
• Subjects: Advanced manufacturing technologies; Algorithms; CAE) and Design; Chatter; Computer-Aided Engineering (CAD; Cutting parameters; Deep learning; Empirical analysis; Engineering; Entropy; Experiments; Frequencies; Frequency response functions; Industrial and Production Engineering; Mathematical models; Mechanical Engineering; Media Management; Methods; Milling (machining); Original Article; Sensors; Signal distribution; Skewness; Stainless steel; Turning (machining); Wavelet transforms
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-024-13203-9

Chatter is a common state in milling and turning, which will reduce the machining quality of parts. For taking adequate measures to avoid chatter, chatter detection is necessary. However, chatter recognition still has many difficulties for both turning and milling. Due to the material removal effect, the chatter frequency will change. The existing frequency band extraction methods, such as ensemble empirical mode decomposition (EEMD) and wavelet packet transform, can not fully reflect the chatter information. As a result, a novel online chatter recognition method is proposed based on spectrum characteristics, which can be applied to the chatter recognition for both turning and milling under the combination of multiple cutting parameters black simultaneously. Firstly, we carried out the hammering experiment to obtain the frequency response function and modal frequency of the machining system before and after machining. The signal distribution characteristics of turning and milling signals are analyzed. Four features sensitive to chatter are extracted, including spectral standard deviation, frequency spectral expectation (FSE), spectral skewness, and relative power spectral entropy (RPE). Taking these features as inputs, a chatter recognition model is established based on an extreme gradient boosting (XGBoost) algorithm. Finally, we conducted the turning and milling tests. The experimental results show that the proposed chatter recognition model can be effective under different cutting parameters for turning and milling. Besides, results also show the chatter recognition model has extraterritorial applicability.