Chatter Detection in Turning Processes Using Coherence of Acceleration and Audio Signals

Hynynen, Katja; Ratava, Juho; Lindh, Tuomo; Rikkonen, Mikko; Ryynänen, Ville; Lohtander, Mika; Varis, Juha

doi:10.1115/1.4026948

Cited by 49 publications

(9 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second one is to generalize the optimization model for selecting optimal geometry parameters of the cutter. The third one is to fuse the present method with some on-line chatter detection techniques [41,42] for high performance milling.…”

Section: Discussionmentioning

confidence: 99%

Harmonic Differential Quadrature Method for Surface Location Error Prediction and Machining Parameter Optimization in Milling

Ding

Zhang

Ding

2015

Journal of Manufacturing Science and Engineering

View full text Add to dashboard Cite

This paper presents a semi-analytical numerical method for sur face location error (SLE) prediction in milling processes, gov erned by a time-periodic delay-differential equation (DDE) in state-space form. The time period is discretized as a set of sam pling grid points. By using the harmonic differential quadrature method (DQM), the first-order derivative in the DDE is approxi mated by the linear sums of the state values at all the sampling grid points. On this basis, the DDE is discretized as a set of alge braic equations. A dynamic map can then be constructed to simul taneously determine the stability and the steady-state SLE of the milling process. To obtain optima! machining parameters, an opti mization model based on the milling dynamics is formulated and an interior point penalty function method is employed to solve the problem. Experimentally validated examples are utilized to verify the accuracy and efficiency of the proposed approach.

show abstract

Section: Discussionmentioning

confidence: 99%

Harmonic Differential Quadrature Method for Surface Location Error Prediction and Machining Parameter Optimization in Milling

Ding

Zhang

Ding

2015

Journal of Manufacturing Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Signal acquisition is usually achieved by the sensor. There are many kinds of sensors that can be used to acquire vibration signal in the machining operation, such as accelerometer [27], dynamometer [28], acoustic emission [29], displacement [30], and electrical power sensors [31]. In this study, the cutting force signals in the X and Y directions are selected to identify chatter, and are measured by a Kistler dynamometer 9257B mounted between the workpiece and the workbench.…”

Section: Signal Acquisition and Feature Extractionmentioning

confidence: 99%

A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine

Peng

Wang

Liao

2015

Journal of Sound and Vibration

View full text Add to dashboard Cite

“…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%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Chatter Detection in Turning Processes Using Coherence of Acceleration and Audio Signals

Cited by 49 publications

References 26 publications

Harmonic Differential Quadrature Method for Surface Location Error Prediction and Machining Parameter Optimization in Milling

Harmonic Differential Quadrature Method for Surface Location Error Prediction and Machining Parameter Optimization in Milling

A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine

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

Contact Info

Product

Resources

About