Analytical prediction of chatter stability for modulated turning

Nam, Soohyun; Eren, Bora; Hayasaka, Takehiro; Sencer, Burak; Shamoto, Eiji

doi:10.1016/j.ijmachtools.2021.103739

Cited by 13 publications

(2 citation statements)

References 29 publications

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“…Urbikain et al [ 2 ] developed a stability model with the Chebyshev collocation method and predicted the chatter straight turning of non-rigid parts. In the frequency domain, a linear stability analysis model for the auxiliary low-frequency vibration during the turning process has been established, along with an analytical stability prediction method for chatter in machining [ 3 ]. Khoshdarregi et al [ 4 ] established a new threading model based on the dynamics of the thin-walled shell to research the multipoint thread-turning process of thin-walled oil pipes.…”

Section: Introductionmentioning

confidence: 99%

Chatter Stability Prediction for Deep-Cavity Turning of a Bent-Blade Cutter

Wang,

Song,

Liu

2024

Sensors

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The bent-blade cutter is widely used in machining typical deep-cavity parts such as turbine discs and disc shafts, but few scholars have studied the dynamics of the turning process. The existing mechanism of regenerative chatter in the metal-cutting process does not consider the influence of bending and torsional vibration, the change of tool profile and the complex machining geometry, so it cannot be directly used to reveal the underlying cause of the chatter phenomena in the deep inner cavity part turning process. This paper attempts to investigate the dynamic problem of the bent-blade cutter turning process. The dynamic model of a bent-blade cutter is proposed by considering the regenerative chatter effect. Based on the extended Timoshenko beam element (E-TBM) theory and finite element method (FEM), the coupling between the bending vibrations and the torsional vibrations, as well as the dynamic cutting forces, are modeled along the turning path. The vibration characteristics of the bending–torsion combination of cutter board and cutter bar, together with the dynamical governing equation, were analyzed theoretically. The chatter stability of a bent-blade cutter with a bending and torsion combination effect is predicted in the turning process. A series of turning experiments are carried out to verify the accuracy and efficiency of the presented model. Furthermore, the influence of cutting parameters on the cutting process is analyzed, and the results can be used to optimize the cutting parameters for suppressing machining vibration and improving machining process stability.

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Section: Introductionmentioning

confidence: 99%

Chatter Stability Prediction for Deep-Cavity Turning of a Bent-Blade Cutter

Wang,

Song,

Liu

2024

Sensors

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“…Accurate real-time chatter detection can prevent damage and excessive tool wear in costly machine tools. There are many kinds of methods proposed to detect chatter precisely and rapidly [7][8][9]. Signal processing is important for the detection of chatter.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Chen-Lee Chaotic System Based Deep Convolutional Generative Adversarial Nets for Chatter Diagnosis

Kuo

Luan

Tseng

et al. 2021

Preprint

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Chatter has a direct effect on the precision and life of machine tools and its detection is a crucial issue in all metal machining processes. Traditional methods focus on how to extract discriminative features to help identify chatter. Nowadays, deep learning models have shown an extraordinary ability to extract data features which are their necessary fuel. In this study deep learning models have been substituted for more traditional methods. Chatter data are rare and valuable because the collecting process is extremely difficult. To solve this practical problem an innovative training strategy has been proposed that is combined with a modified convolutional neural network and deep convolutional generative adversarial nets. This improves chatter detection and classification. Convolutional neural networks can be effective chatter classifiers, and adversarial networks can act as generators that produce more data. The convolutional neural networks were trained using original data as well as by forged data produced by the generator. Original training data were collected and preprocessed by the Chen-Lee chaotic system. The adversarial training process used these data to create the generator and the generator could produce enough data to compensate for the lack of training data. The experimental results were compared with without a data generator and data augmentation. The proposed method had an accuracy of 95.3% on leave-one-out cross-validation over ten runs and surpassed other methods and models. The forged data were also compared with original training data as well as data produced by augmentation. The distribution shows that forged data had similar quality and characteristics to the original data. The proposed training strategy provides a high-quality deep learning chatter detection model.

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