A synthetic criterion for early recognition of cutting chatter

Jia, Guangfei; Wu, Bo; Hu, Youmin; Xie, Fei; Liu, Ao

doi:10.1007/s11431-013-5360-9

Cited by 18 publications

(6 citation statements)

References 21 publications

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“…A distribution-based criterion was proposed as a threshold value independent of cutting conditions [80,118]. Jia et al [202] designed a synthetic criterion (SC) that mixes standard deviation and autocorrelation function. Similarly, the multi-dimensional indicator (Q-factor) uses the centre frequencies and the oscillatory characteristics of the signal [129], and it was employed in mirror milling [123].…”

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

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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.

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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

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“…In monitoring the milling process, the relationship between vibration signals and machine states is typically established with features in either the time or frequency domain. Example features are a normalized energy ratio [9], statistical features [10], wavelet packet coefficients [11], and time and frequency distributions [12,13]. Extracted features are then classified before they can be used to recognize the cutting states.…”

Section: Measurement Science and Technologymentioning

confidence: 99%

“…Training samples1,2,5,6,11,12,13,15,16,20,23,24,27 Testing samples3,4,7,8,9,10,14,17,18,19,21,22,25,26 …”

mentioning

confidence: 99%

An integrated condition-monitoring method for a milling process using reduced decomposition features

Liu

Wang

et al. 2017

Meas. Sci. Technol.

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Complex and non-stationary cutting chatter affects productivity and quality in the milling process. Developing an effective condition-monitoring approach is critical to accurately identify cutting chatter. In this paper, an integrated condition-monitoring method is proposed, where reduced features are used to efficiently recognize and classify machine states in the milling process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition, and Shannon power spectral entropy is calculated to extract features from the decomposed signals. Principal component analysis is adopted to reduce feature size and computational cost. With the extracted feature information, the probabilistic neural network model is used to recognize and classify the machine states, including stable, transition, and chatter states. Experimental studies are conducted, and results show that the proposed method can effectively detect cutting chatter during different milling operation conditions. This monitoring method is also efficient enough to satisfy fast machine state recognition and classification.

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“…The chatter signal during machining has complex nonlinear and non-stationary characteristics. So, authors employed nonlinear and non-stationary signal processing methods in the time domain [3] [4], frequency domain [5], and time-frequency domain [6][7] [8] to detect the early chatter.…”

Section: Introductionmentioning

confidence: 99%

“…In the time domain, the chatter feature can be extracted by calculating the statistical characteristics of the collected signal, such as mean, variance, and standard deviation [3]. Jia et al [4] presented a synthetic criterion (SC) by integrating standard deviation and one-step auto-correlation function for early chatter detection. The experimental results verified that SC can detect the early chatter.…”

Section: Introductionmentioning

confidence: 99%

MaxEnt feature-based reliability model method for real-time detection of early chatter in high-speed milling

et al. 2021

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Real-time detection of early chatter is a vital strategy to improve machining quality and material removal rate in the high-speed milling processes. This paper proposes a maximum entropy (MaxEnt) feature-based reliability model method for real-time detection of early chatter based on multiple sampling per revolution (MSPR) technique and second-order reliability method (SORM). To enhance the detection reliability, the MSPR is used to acquire multiple sets of once-per-revolution sampled data (i.e., MSPR data) and to overcome the shortcoming of the once-per-revolution sampling. The proposed MaxEnt feature-based reliability model method solves the issue of the real-time detection of early chatter while ensuring its reliability. The failure hazard function (FHF) is estimated as a chatter indicator by using the SORM with the MaxEnt feature. The proposed method consists of five steps. First, set the prior parameters. Then collect data by using the MSPR technique. Next, calculate a set of the standard deviation of the data collected as a chatter feature and estimate the chatter indicator FHF by applying the SORM with the MaxEnt feature. Finally, implement the real-time detection of early chatter based on the estimated chatter indicator FHF and the threshold FHF0. The proposed method is applied to the high-speed milling process. Two examples prove that the proposed method can detect two kinds of early chatter: the early-stage of a severe chatter and the slightly intolerable chatter.

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A synthetic criterion for early recognition of cutting chatter

Cited by 18 publications

References 21 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

An integrated condition-monitoring method for a milling process using reduced decomposition features

MaxEnt feature-based reliability model method for real-time detection of early chatter in high-speed milling

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