Chatter detection for milling using novel p -leader multifractal features
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Chatter detection for milling using novel p-leader multifractal features Yun Chen1
· Huaizhong Li2 · Liang Hou1
· Xiangjian Bu1 · Shaogan Ye1 · Ding Chen3
Received: 25 March 2020 / Accepted: 18 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Chatter detection · Milling processes · Multifractal features · p-leader · Feature selection
Introduction Chatter in machining is a self-excited vibration between cutting tool and workpiece, which is detrimental due to abnormal vibration increases. The increased vibration results in poor surface finish, severe tool wear, tool breakage, and even damage to machine tools. In order to avoid chatter, theoretical modelling of the machining process can be used to select cutting parameters from stability diagrams. The zerothorder (Altintas and Budak 1995) and semi-discrete (Insperger and Stepan 2004) are typical methods to determine stability diagrams. However, a solid knowledge background about machining dynamics is needed for stability diagrams, and is not practical for industrial users to operate. Moreover, these models often do not account for the effect of the changing dynamics or highly complex machining operations (Yesilli
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Liang Hou [email protected]
1
Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, China
2
School of Engineering and Built Environment, Griffith University, Gold Coast Campus, Gold Coast, QLD 4222, Australia
3
School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361024, China
et al. 2020). Given the current development of sensory techniques and advanced signal processing methods, early chatter detection becomes an alternative to efficiently monitor and control the machining condition. Force, vibration and acoustic emission signals are often used for chatter
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