Detection of chatter vibration in end milling applying disturbance observer

Kakinuma, Yasuhiro; Sudo, Yui; Aoyama, Tojiro

doi:10.1016/j.cirp.2011.03.080

Cited by 73 publications

(23 citation statements)

References 6 publications

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“…A DOB [4,5] is available for cancellation of a disturbance and dramatic enhancement of the robustness in motor control. Disturbance force under a single-mass model of the linear motor driven stage is defined as the sum of an external load force F l [N]; friction force in guideways, F fric [N]; and fluctuating forces caused by variations of the thrust coefficient and mass variation.…”

Section: Position and Force Control Based On Dobmentioning

confidence: 99%

Development of 5-axis polishing machine capable of simultaneous trajectory, posture, and force control

et al. 2013

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Section: Position and Force Control Based On Dobmentioning

confidence: 99%

Development of 5-axis polishing machine capable of simultaneous trajectory, posture, and force control

et al. 2013

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“…For example, Kakinuma et al [7] experimentally showed that the chatterinduced fluctuation in the cutting torque in milling can be detected only from the servo information in a spindle control system. Furthermore, our previous work showed that fractureinduced fluctuations in drilling can also be detected from the servo information of ballscrew-driven stage control systems [8].…”

Section: Introductionmentioning

confidence: 98%

A sensorless approach for tool fracture detection in milling by integrating multi-axial servo information

2016

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“…The majority of available in-process methods for chatter identification rely on extracting certain features from the acoustic, vibration, or force signals and comparing them against some predefined markers of chatter [12,13,14,15,8,16,17,18,19,20,21,22]. They can be broadly categorized into two groups as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

On transfer learning for chatter detection in turning using wavelet packet transform and ensemble empirical mode decomposition

Yesilli

Khasawneh

Otto

2020

CIRP Journal of Manufacturing Science and Technology

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The increasing availability of sensor data at machine tools makes automatic chatter detection algorithms a trending topic in metal cutting. Two prominent and advanced methods for feature extraction via signal decomposition are Wavelet Packet Transform (WPT) and Ensemble Empirical Mode Decomposition (EEMD). We apply these two methods to time series acquired from an acceleration sensor at the tool holder of a lathe. Different turning experiments with varying dynamic behavior of the machine tool structure were performed. We compare the performance of these two methods with Support Vector Machine (SVM) classifier combined with Recursive Feature Elimination (RFE). We also show that the common WPT-based approach of choosing wavelet packets with the highest energy ratios as representative features for chatter does not always result in packets that enclose the chatter frequency, thus reducing the classification accuracy. Further, we test the transfer learning capability of each of these methods by training the classifier on one of the cutting configurations and then testing it on the other cases. It is found that when training and testing on data from the same cutting configuration both methods yield high accuracies reaching in one of the cases as high as 94% and 91%, respectively, for WPT and EEMD. However, EEMD is shown to outperform WPT in transfer learning applications with accuracy of up to 84%. Therefore, for systems where the movement of the cutting center leads to significant variations in the stiffness of the machine-tool system, we recommend using EEMD over WPT for training a classifier. This is because EEMD retains higher accuracy rates in comparison to WPT when the input data stream deviates from the data that was used to train the classifier.

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Detection of chatter vibration in end milling applying disturbance observer

Cited by 73 publications

References 6 publications

Development of 5-axis polishing machine capable of simultaneous trajectory, posture, and force control

Development of 5-axis polishing machine capable of simultaneous trajectory, posture, and force control

A sensorless approach for tool fracture detection in milling by integrating multi-axial servo information

On transfer learning for chatter detection in turning using wavelet packet transform and ensemble empirical mode decomposition

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