Abstract:Many types of sensor have been utilized to monitor milling vibration, and many analysis methods are devoted to the investigation of milling vibration or milling dynamics. In this work, a noncontact sensor and a time-frequency domain analysis method were applied to identify the state of milling vibration. A microphone was employed in practical tests to record the milling dynamics. The Teager-Huang transform (THT) was adopted for the acoustic signal analysis owing to its high resolution in the time-frequency dom… Show more
“…Energy operators, like the squared energy and Teager-Kaiser operators, have been used in chatter detection to estimate instantaneous frequency and amplitude, along with diverse decomposition methods [179,189]. Lee et al [168] utilized the Teager-Huang transform (THT), which combines EMD with the Teager energy operator. Moreover, novel methods have been considered as an alternative to VMD.…”
Section: Time-frequency Domain Analysismentioning
confidence: 99%
“…* Two or more signal processing methods combined[91,136,152,159,165,179,192,196,220,348] Other methods[72,73,84,95,101,135,137,147,148,150,155,157,161,164,168,173,190,227,258,259] …”
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.
“…Energy operators, like the squared energy and Teager-Kaiser operators, have been used in chatter detection to estimate instantaneous frequency and amplitude, along with diverse decomposition methods [179,189]. Lee et al [168] utilized the Teager-Huang transform (THT), which combines EMD with the Teager energy operator. Moreover, novel methods have been considered as an alternative to VMD.…”
Section: Time-frequency Domain Analysismentioning
confidence: 99%
“…* Two or more signal processing methods combined[91,136,152,159,165,179,192,196,220,348] Other methods[72,73,84,95,101,135,137,147,148,150,155,157,161,164,168,173,190,227,258,259] …”
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.
“…In this context, techniques based on the sensing of acoustic emission (AE) phenomena have gained increasing interest during the last few years due to their simplicity of use and the ability to monitor a wide variety of features, such as teeth breakage [ 7 , 8 ], runout [ 9 , 10 ] and chattering [ 11 , 12 ]. In particular, those techniques based on the use of noncontact sensors in the range of audible sound, such as different types of microphones [ 13 , 14 ], are particularly attractive due to the reduced intrusiveness, so they have been used for detecting the chattering phenomenon [ 15 , 16 , 17 ] and tool condition monitoring in conventional machining [ 18 , 19 ], high-speed machining [ 20 ] and abrasion processes [ 21 ].…”
This paper presents the implementation of a measurement system that uses a four microphone array and a data-driven algorithm to estimate depth of cut during end milling operations. The audible range acoustic emission signals captured with the microphones are combined using a spectral subtraction and a blind source separation algorithm to reduce the impact of noise and reverberation. Afterwards, a set of features are extracted from these signals which are finally fed into a nonlinear regression algorithm assisted by machine learning techniques for the contactless monitoring of the milling process. The main advantages of this algorithm lie in relatively simple implementation and good accuracy in its results, which reduce the variance of the current noncontact monitoring systems. To validate this method, the results have been compared with the values obtained with a precision dynamometer and a geometric model algorithm obtaining a mean error of 1% while maintaining an STD below 0.2 mm.
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