Chatter detection in milling process based on the combination of wavelet packet transform and PSO-SVM

Zheng, Qinsi; Chen, Guangsheng; Jiao, Anling

doi:10.1007/s00170-022-08856-3

Cited by 28 publications

(8 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some optimization methods have been used for the selection of various parameters in the signal Fig. 17 CNN architecture for chatter detection [159] processing and training of AI as a classification model, as reported in [96,121,133,136,145,166,176,181,188,192,234,317,343], identifying the recurrent application of PSO and GA. Tran et al [154] proposed to use of a similarity classifier, as the performance of conventional approaches is greatly dependent on the parameters adjustment. Zhang et al [112] advised that the selection of decomposition parameters highly affects the chatter sensitivity.…”

Section: Discussion Of Classification Modelsmentioning

confidence: 99%

Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing.

show abstract

Section: Discussion Of Classification Modelsmentioning

confidence: 99%

Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…Characteristics are usually defined manually, which requires a great deal of human expertise. Among the machine learning methods used in automatic chatter, detection is the majority of identification techniques that rely on Support Vector Machines (SVM) [87], Artificial Neural Networks (ANN) [59] [88], unsupervised Learning [89], models of deep learning like the convolutional neural network. Further study will show the growth of words over the years.…”

Section: Machine Learningmentioning

confidence: 99%

Review of AI-based methods for chatter detection in machining based on bibliometric analysis

Kounta

Arnaud

Kamsu-Foguem

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

HIGHLIGHTS• Bibliometric analysis on chatter detection techniques in machining processes.• Effectiveness of AI methods combined with transformation and decomposition techniques.• Research areas mainly cover manufacturing, mechanics, and automation control systems.• Application of signal processing techniques in chatter detection with their advantages.• Challenges of deep learning models to solve problems of performance and explainability.

show abstract

“…Zheng et al [17] used permutation entropy as a feature vector and then used SVM as a classifier to achieve fault diagnosis of rolling bearings. However, the classification performance of SVM depends mainly on the selection of model parameters [18,19]. Li et al [11] utilized the idea of ensemble learning and constructed an AdaBoost model for fault diagnosis of wind turbine bearings by using boosting ensemble method to combine multiple decision trees.…”

Section: Introductionmentioning

confidence: 99%

A bearing fault diagnosis method with improved symplectic geometry mode decomposition and feature selection

Chen,

Zheng

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

A rolling bearing fault diagnosis method based on improved symplectic geometry mode decomposition and feature selection was proposed to solve the problem of low fault identification due to the influence of noise on early bearing fault features. First, the symplectic geometry mode decomposition is improved to enhance its robustness in decomposing signals with noise, then the time domain, frequency domain, and time-frequency features of each symplectic geometric component are extracted as feature vectors. Second, a comprehensive feature selection strategy is proposed to select the optimal subset of features that are conducive to fault classification. Finally, considering the problem of low classification accuracy of a single machine learning model, the AdaBoost-WSO-SVM model is constructed for fault classification using the AdaBoost algorithm of integrated learning. Experimental decomposition of complex signals with noise indicates that the improved symplectic geometry mode decomposition is more effective compared to traditional symplectic geometry mode decomposition. Subsequently, multiple experiments were conducted using the bearing datasets from Case Western Reserve University (CWRU) and Jiangnan University (JNU). The experimental results reveal that, after comprehensive feature selection and ensemble learning pattern recognition experiments on the CWRU dataset, the average accuracy of fault diagnosis can reach 99.67%. On the JNU dataset, the proposed fault diagnosis method achieves an average accuracy of 95.03%. This suggests that, compared to other feature selection methods and classification models, the proposed approach in this paper exhibits higher accuracy and generalization capabilities.

show abstract

Chatter detection in milling process based on the combination of wavelet packet transform and PSO-SVM

Cited by 28 publications

References 47 publications

Chatter detection in milling processes—a review on signal processing and condition classification

Chatter detection in milling processes—a review on signal processing and condition classification

Review of AI-based methods for chatter detection in machining based on bibliometric analysis

A bearing fault diagnosis method with improved symplectic geometry mode decomposition and feature selection

Contact Info

Product

Resources

About