Chatter detection in milling processes—a review on signal processing and condition classification

Abstract

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.

A Review of Intelligent Condition Monitoring of Rolling Element Bearings

“…WT converts the time domain signals into a group of wave-like signals, from which the original data can be reassembled using the weighting coefficient of each signal (i.e. wavelet coefficients) [70]. WT-based methods have been widely used in bearing condition monitoring.…”

Identification of end-milling chatter based on comprehensive feature fusion

“…STFT is a technique for analysing a signal's time-varying frequency content [24,25]. In reality, the small window is moved along the signal while a particular amount of overlap exists between successive windows to calculate the STFT.…”

Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review

“…These changes frequently occur in cutting conditions at the beginning and end of a cutting engagement, as well as when the feed direction changes, and may result in temporarily unstable vibration conditions, to which physics-based chatter detection methods are particularly sensitive. The second strategy to detect chatter is to develop data-driven models, where external sensors such as accelerometers, AE sensors, and dynamometers have been used to directly detect chatter in machining operations [177,178]. Signal-based data-driven chatter detection algorithms can be employed in real-time control systems to actively suppress chatter vibrations through modifying the spindle speed.…”

“…A possible improvement is to combine a data-driven model with a physics-based model to enhance the generality of the approach, as recently introduced in [181]. It is im- The second strategy to detect chatter is to develop data-driven models, where external sensors such as accelerometers, AE sensors, and dynamometers have been used to directly detect chatter in machining operations [177,178]. Signal-based data-driven chatter detection algorithms can be employed in real-time control systems to actively suppress chatter vibrations through modifying the spindle speed.…”