Vibration singularity analysis for milling tool condition monitoring

Zhou, Chang’an; Yang, Bin; Guo, Kai; Liu, Jiangwei; Sun, Jie; Song, Ge; Zhu, Shaowei; Sun, Chao; Jiang, Zhenxi

doi:10.1016/j.ijmecsci.2019.105254

Cited by 78 publications

(29 citation statements)

References 29 publications

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“…The selection of wavelet basis functions is critical for the singularity analysis of sensory signals. Zhou et al [24], [25] found the wavelet basis with two vanishing moments were the most appropriate wavelet to detect the singularities in the vibration signals of the milling process. And Gaussian wavelet can ensure the continuity of the modulus maxima line [18].…”

Section: Results and Discussion A He Estimation Proceduresmentioning

confidence: 99%

“…Holder Exponent (HE), estimated from the cutting force to classify different tool statuses in micro-milling. Zhou et al [24], [25] studied the difference in adopting singularity analysis to estimate the cutting force and vibration of the milling process, and established a TCM approach with good performances. However, there are few researches on employing singularity features to establish tool tipping monitoring models published.…”

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confidence: 99%

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

et al. 2020

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The gradual tool wear is unavoidable in the machining process, it directly influences the surface integrity and dimensional tolerances of the components. The tool condition monitoring (TCM) systems have the capacity to make full use of the cutting potential of the cutting tools, which is of great significance for improving production efficiency and ensuring product quality. In milling titanium alloys, tool tipping is one of the main failure modes. Therefore, this study focuses on establishing a tool tipping monitoring approach for the milling process. The singularity analysis method based on wavelet transform was employed to characterize the variation of vibration waveforms quantitatively with the Holder Exponent (HE) index. The probability density distribution and statistical analysis were adopted to extract effective HE features from the HE indexes to correlate with the different tool conditions. The mutual information method was adopted to rank the discriminability of the HE statistical parameters. Then, several machine learning (ML) models were established with the screened HE features. Finally, the results of the experiments indicate that the Support Vector Machine (SVM) model has the highest classification accuracy and can provide practical guides on the tool changes.INDEX TERMS Tool tipping, titanium alloys, vibrations, Holder Exponents, support vector machine. I. INTRODUCTION

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Section: Results and Discussion A He Estimation Proceduresmentioning

confidence: 99%

mentioning

confidence: 99%

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

et al. 2020

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“…The cutting tool vibrations are measured by piezoelectric or micro-electromechanical system MEMS accelerometers to predict the tool edge wear and the surface roughness of the machined surface, among others [ 34 , 35 ]. Sharp cutting tools create modest amount of vibrations that rise as the tool condition deteriorates [ 36 ].…”

Section: Sensing and Data Acquisitionmentioning

confidence: 99%

“…This domain has attracted considerable attention for TCM systems compared to the aforementioned domains [ 2 , 18 , 25 ]. Time–frequency representation of the acquired data is constructed using the continuous wavelet transform (CWT), discrete wavelet transform (DWT), wavelet packet transform (WPT), short-time Fourier transform (STFT), or empirical mode decomposition (EMD) algorithms [ 29 , 34 , 145 , 146 ]. Extracted features include the average energy of wavelet coefficients and their wavelet domain statistics (RMS, mean, and variance, etc.)…”

Section: Signal Processing Techniquesmentioning

confidence: 99%

“…Despite the utilization of the most advanced conventional and deep learning algorithms, no comprehensive, reliable, and friendly solution to monitor tool wear in real-time has been found yet in the open literature to satisfy the industrial environment requirements. They are commonly trained and validated on a single machine tool, with a single cutting tool-workpiece combination, and under a limited set of cutting conditions [ 22 , 232 ], as investigated in [ 34 , 35 , 146 , 233 ]. This led to the absence of generalized discriminative features that are capable of describing the tool condition under a variety of processes and cutting conditions [ 4 ].…”

Section: Decision Making For Tool Wear Monitoringmentioning

confidence: 99%

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Tool Condition Monitoring for High-Performance Machining Systems—A Review

Mohamed

Hassan

M’Saoubi³

et al. 2022

Sensors

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In the era of the “Industry 4.0” revolution, self-adjusting and unmanned machining systems have gained considerable interest in high-value manufacturing industries to cope with the growing demand for high productivity, standardized part quality, and reduced cost. Tool condition monitoring (TCM) systems pave the way for automated machining through monitoring the state of the cutting tool, including the occurrences of wear, cracks, chipping, and breakage, with the aim of improving the efficiency and economics of the machining process. This article reviews the state-of-the-art TCM system components, namely, means of sensing, data acquisition, signal conditioning and processing, and monitoring models, found in the recent open literature. Special attention is given to analyzing the advantages and limitations of current practices in developing wireless tool-embedded sensor nodes, which enable seamless implementation and Industrial Internet of Things (IIOT) readiness of TCM systems. Additionally, a comprehensive review of the selection of dimensionality reduction techniques is provided due to the lack of clear recommendations and shortcomings of various techniques developed in the literature. Recent attempts for TCM systems’ generalization and enhancement are discussed, along with recommendations for possible future research avenues to improve TCM systems accuracy, reliability, functionality, and integration.

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