Estimation and improvement of cutting safety

Yan, Yao; Xu, Jian; Wiercigroch, Marian

doi:10.1007/s11071-019-04980-0

Cited by 14 publications

(7 citation statements)

References 52 publications

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“…For any point in the workpiece surface, one pass of a cutting tool leaves a mark recording the tool displacement, which perturbs the tool motion after one workpiece revolution. This regenerative effect gradually induces periodic vibration when energy dissipation of the turning system is insufficient, leaving chatter marks on the whole 65 workpiece surface [20]. Similar phenomena can also been found in other cutting operations, such as milling, grinding and drilling processes [21,22,23].…”

supporting

confidence: 58%

Dynamics of rotary drilling with non-uniformly distributed blades

Yan

Wiercigroch

2019

International Journal of Mechanical Sciences

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supporting

confidence: 58%

Dynamics of rotary drilling with non-uniformly distributed blades

Yan

Wiercigroch

2019

International Journal of Mechanical Sciences

Self Cite

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“…A most important characteristic of any chaotic system is that a very small change of the control system input parameter will cause a great change in output. Through the controller, see equation (2), the dynamic error between the master and slave chaotic systems, see equation (3), forms a strange attractor to maintain the chaotic characteristics.…”

Section: A Data Preprocessingmentioning

confidence: 99%

“…Chatter, the vibrations that sometimes arise between the cutting tools and the workpiece during machining, can result in unacceptable degradation of product quality [1]. Chatter can also damage both the cutting tools and the machine itself [2]. There are two types of chatter, primary and secondary.…”

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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“…However, the focus was mainly on stability criteria for linear systems [9,30,60,65,84], be it Lyapunov-Krasovskii functionals [28,35,74], Lyapunov-Razumikhin functions [35], comparison principles [17,33,64], inputoutput approaches [8], or eigenvalue calculations [7,26,41,43]. By contrast, in technical applications, we encounter nonlinear systems [22,23,68,72,85]. Indeed, for a nonlinear system the Principle of Linearized Stability [21] allows to deduce stability or instability of equilibria, but the question arises: what is the practical relevance of knowledge about asymptotic stability if there is no knowledge about the domain of attraction?…”

Section: Introductionmentioning

confidence: 99%

“…Remark 4 (Numerical Errors) All results on the domain of attraction that are based on time simulations[2,18,55,57,72,76,82,85] rely on the distinction between convergence and divergence from numerical results. However, this asymptotic behavior t → ∞ is usually not covered by any numerical bounds, and thus the classification cannot be considered as proven.…”

mentioning

confidence: 99%

On norm-based estimations for domains of attraction in nonlinear time-delay systems

2020

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For nonlinear time-delay systems, domains of attraction are rarely studied despite their importance for technological applications. The present paper provides methodological hints for the determination of an upper bound on the radius of attraction by numerical means. Thereby, the respective Banach space for initial functions has to be selected and primary initial functions have to be chosen. The latter are used in timeforward simulations to determine a first upper bound on the radius of attraction. Thereafter, this upper bound is refined by secondary initial functions, which result a posteriori from the preceding simulations. Additionally, a bifurcation analysis should be undertaken. This analysis results in a possible improvement of the previous estimation. An example of a time-delayed swing equation demonstrates the various aspects.

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Estimation and improvement of cutting safety

Cited by 14 publications

References 52 publications

Dynamics of rotary drilling with non-uniformly distributed blades

Dynamics of rotary drilling with non-uniformly distributed blades

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

On norm-based estimations for domains of attraction in nonlinear time-delay systems

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