Identification of milling chatter based on a novel frequency-domain search algorithm

Liu, Chang; Xu, Weiwei; Gao, Lei

doi:10.1007/s00170-020-05789-7

Cited by 15 publications

(4 citation statements)

References 38 publications

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“…A multiscale method was utilized by Li et al with acceleration signals [134] and force signals [157], obtaining accurate results in both cases. Likely, Chang et al [143] showed that their approach was suitable for both types. In summary, force signals have been employed in chatter detection in the studies [73-77, 84, 86, 101, 112, 114-116, 123, 126, 130, 131, 133, 137, 140, 143, 144, 148, 154, 155, 157, 159, 162, 167, 170, 173, 184-186, 193, 204, 227, 232-236]…”

Section: Dynamometer-cutting Forcesmentioning

confidence: 99%

“…In milling, chatter occurs in a bandwidth close to a natural frequency or its harmonics. It may be modulated by the fundamental frequencies [112], due to the system dynamics and the cutting conditions [143]. Moreover, milling processes have multiple chatter frequencies, while turning usually has a specific chatter frequency [206].…”

Section: Stability Diagram and Chatter Predictionmentioning

confidence: 99%

“…Jo et al [253] proposed the sum of frequency components in a high-frequency band to later extract statistical features to identify chatter. Chang et al [143] searched the vibration frequencies under diverse cutting conditions and identified chatter occurrence without needing a threshold. Jeong et al [236] employed a liftered spectrum, while Shimana et al [191] suggested the pseudo-auto-correlation function as a faster alternative.…”

Section: Frequency Domain Analysismentioning

confidence: 99%

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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: Dynamometer-cutting Forcesmentioning

confidence: 99%

Section: Stability Diagram and Chatter Predictionmentioning

confidence: 99%

Section: Frequency Domain Analysismentioning

confidence: 99%

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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

“…Due to some simplifications in constructing the finite element model, the frequency response function obtained by the finite element simulations still have some obvious deviations from the actual values. Wang [13] introduced the receptance coupling substructure analysis (RCSA) method to utilize the tested FRFs at the end of the spindle to predict the tip FRFs of different tool-holder assemblies. However, the accuracy of RCSA is subject to many parameters that are difficult to measure or identify.…”

Section: Introductionmentioning

confidence: 99%

Efficient Prediction of Stability Boundaries in Milling Considering the Variation of Tool Features and Workpiece Materials

Sun,

Ding,

Deng

et al. 2023

Sensors

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Theoretical stability analysis is a significant approach to predicting chatter-free machining parameters. Accurate milling stability predictions highly depend on the dynamic properties of the process system. Therefore, variations in tool and workpiece attributes will require repeated and time-consuming experiments or simulations to update the tool tip dynamics and cutting force coefficients. Considering this problem, this paper proposes a transfer learning framework to efficiently predict the milling stabilities for different tool–workpiece assemblies through reducing the experiments or simulations. First, a source tool is selected to obtain the tool tip frequency response functions (FRFs) under different overhang lengths through impact tests and milling experiments on different workpiece materials conducted to identify the related cutting force coefficients. Then, theoretical milling stability analyses are developed to obtain sufficient source data to pre-train a multi-layer perceptron (MLP) for predicting the limiting axial cutting depth (aplim). For a new tool, the number of overhang lengths and workpiece materials are reduced to design and perform fewer experiments. Then, insufficient stability limits are predicted and further utilized to fine-tune the pre-trained MLP. Finally, a new regression model to predict the aplim values is obtained for target tool–workpiece assemblies. A detailed case study is developed on different tool–workpiece assemblies, and the experimental results validate that the proposed approach requires fewer training samples for obtaining an acceptable prediction accuracy compared with other previously proposed methods.

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Detection and visualization of chatter in gear hobbing based on combination of time and frequency domain analysis

Tian

Chen

Hong

et al. 2020

Int J Adv Manuf Technol

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Identification of milling chatter based on a novel frequency-domain search algorithm

Cited by 15 publications

References 38 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

Efficient Prediction of Stability Boundaries in Milling Considering the Variation of Tool Features and Workpiece Materials

Detection and visualization of chatter in gear hobbing based on combination of time and frequency domain analysis

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