The Monitoring of Milling Tool Tipping by Estimating Holder Exponents of Vibration

Zhou, Chang’an; Jiang, Zhenxi; Sun, Chao; Zhu, Zhaoju

doi:10.1109/access.2020.2996607

Cited by 16 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Topological features and similarity measure were proposed as they are noise-robust, as shown in turning experiments [358,359], and in simulated signals of end milling by Yesilli et al [263]. Among other candidate features, there is the Holder exponent of vibration signals [360], while diverse wavelet packetbased features are reported in turning [361][362][363], including a proposed multiscale wavelet packet entropy (MWPE) to detect chatter regardless of the beat effect [364].…”

Section: Feature Generationmentioning

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: Feature Generationmentioning

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

“…The main feature selection methods include feature elimination, mutual information, and the Fisher criterion [9][10][11] . Jiang et al [12] screened the characteristics of HE statistical parameters by mutual information method and established multiple machine learning models. The experimental results show that the classification accuracy of the tool wears monitoring model based on SVM is the highest.…”

Section: Introductionmentioning

confidence: 99%

Tool wear condition monitoring method based on Relevance Vector Machine

Jia¹,

Yue

Liu³

et al. 2023

Preprint

View full text Add to dashboard Cite

During the machining process, the tool wear state is closely related to the quality of the workpiece, which will directly affect the performance of the equipment. Not timely replacement of tools will lead to increased processing costs, low workpiece surface quality, and even damage to processing equipment. Therefore, research on tool wear monitoring is necessary for the tool processing industry. By analyzing the relationship between tool wear and sensor signals to determine the required acquisition signal. Aiming at the problem that the original sensor data cannot be directly used in the machining process, the signal processing technology is used to preprocess the original signal, remove the invalid signal collected during the cutting process, and use the filtering method to eliminate the singular points in the original signal. The time domain and frequency domain features of the data are extracted. Firstly, the features are optimized by the extreme random tree(ET), and the tool wear is taken as the target vector. The Pearson correlation coefficient(PCC) between the target vector and the filtered features is calculated, and the features with solid correlation with the target vector are selected. The results show that the relevance vector machine(RVM) model proposed in the research can effectively monitor tool wear.

show abstract

“…Fisher's linear discriminant analysis (Fisher's Score) [20], Fisher's discriminant ratio (FDR) [21,22] and mutual information (MI) [23]are widely used to select the salient features for representing tool wear status in their studies, but the characteristics of features that extraction in TCM research has been ignored. Li et al [24] proposed a novel criterion to measure the correlation between the feature and the tool wear, in which the fluctuation component had been regarded as noise and acted as a penalty component.…”

Section: Introductionmentioning

confidence: 99%

Tool condition monitoring for cavity milling based on bispectrum analysis and Bayesian optimized SVM

Li¹,

Wang

Hu³

et al. 2023

Preprint

View full text Add to dashboard Cite

Tool wear status seriously affects the dimensional accuracy and surface quality of the machined parts. Therefore, tool condition monitoring (TCM) is essential in the milling process of aerospace structural parts due to the use of difficult-to-cut materials and complex cutting trajectories. The Higher order spectrum (HOS) was first employed to analyze the vibration signals, and then bispectral features extracted from de-noised signals were used to characterize the tool wear status. The improved Pearson’s correlation coefficient was used for feature selection to reduce the influence of periodic components on feature selection process. Furthermore, a novel objective function was proposed to guide the hyperparameters optimization process of support vector machine based on Bayesian optimization algorithm, in which the effect of imbalanced data on the recognition rates was considered. To demonstrate the effectiveness of the proposed method, a structural part milling experiment was performed on a vertical machining center and vibration signals of spindle were collected. Based on this, an online TCM model was established. The present study suggests that the proposed TCM system is accurate and robust.

show abstract

The Monitoring of Milling Tool Tipping by Estimating Holder Exponents of Vibration

Cited by 16 publications

References 33 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

Tool wear condition monitoring method based on Relevance Vector Machine

Tool condition monitoring for cavity milling based on bispectrum analysis and Bayesian optimized SVM

Contact Info

Product

Resources

About