Online chatter monitor system based on rapid detection method and wireless communication

Mei, Xueyu; Xu, Haoming; Feng, Pingfa; Meng, Yuan; Xu, Chao; Ma, Yuan; Feng, Feng

doi:10.1007/s00170-022-09941-3

Cited by 2 publications

(1 citation statement)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other physical parameters have been utilized such as displacement measurement [129,131,139] and image of the machined surface [161,244], which has gained wide attention recently due to the emerging of DL techniques. Other innovative sensor alternatives are the use of thinfilms [245][246][247], embedded devices [82], workpiece sensor-integrated fixtures [229,248] and wireless sensors [99,[249][250][251]. Ma et al [246] employed a thin-film piezoelectric and obtained the same detection accuracy as the use of a dynamometer and higher accuracy than an accelerometer and a microphone.…”

Section: Other Sensors and Simulated Signalsmentioning

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: Other Sensors and Simulated Signalsmentioning

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

A Rapid Method to Quantify High-Frequency-Dominant Signals Based on Fixed-Interval Fractal Dimension

Feng,

Song,

Zhang

et al. 2024

Fractal Fract

View full text Add to dashboard Cite

The fractal dimension (FD) is an effective indicator to characterize various signals in engineering. However, the FD is nearly twice that of its maximum value when examining high-frequency-dominant signals, such as those in milling chatter. Previous studies in the literature have generally employed signal-pre-processing methods that require a significant amount of time to lower the FD range, thus enabling the distinguishment of different states while disabling online monitoring. A new quantitative method based on the FD within a fixed interval was constructed in this study to address this issue. First, the relationship between the fixed-interval fractal dimension (FFD) and the energy ratio (ER), named the fractal complexity curve (FC-Curve), was established, and the sensitivity region of the FFD was determined. Second, a high-frequency suppression filter (HSF) with a high calculation speed was proposed to suppress the signal’s ER so the FFD could be adjusted within its sensitivity region. Moreover, a fast energy ratio (FER) correlated with the FFD was proposed using the FC-Curve and HSF to quantitatively analyze dominant high-frequency signals. Finally, the proposed method was verified via its application in milling chatter identification. The FER method accomplished signal analysis more quickly than the traditional energy ratio difference and entropy methods, demonstrating its feasibility for online monitoring and chatter suppression in practical engineering applications.

show abstract

Online chatter monitor system based on rapid detection method and wireless communication

Cited by 2 publications

References 44 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

A Rapid Method to Quantify High-Frequency-Dominant Signals Based on Fixed-Interval Fractal Dimension

Contact Info

Product

Resources

About