Novel fractional-order convolutional neural network based chatter diagnosis approach in turning process with chaos error mapping

Kuo, Ping‐Huan; Tseng, Yung‐Ruen; Luan, Po‐Chien; Yau, Her‐Terng

doi:10.1007/s11071-023-08252-w

Cited by 36 publications

(3 citation statements)

References 34 publications

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“…In CNC turning, Ibarra-Zarate et al [ 29 ] used 2D CNNs on images of the Mel-Frequency Cepstral Coefficients obtained from acoustic emissions to predict surface roughness. Kuo et al [ 30 ] applied 2D CNNs on fractional order chaos maps of vibration data for chatter detection. Hanchate et al [ 31 ] proposed a framework for predicting average surface roughness in CNC grinding, employing 2D CNNs on time–frequency spectrogram frames of vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring

Stathatos,

Tzimas,

Benardos

et al. 2024

Sensors

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This study addresses the need for advanced machine learning-based process monitoring in smart manufacturing. A methodology is developed for near-real-time part quality prediction based on process-related data obtained from a CNC turning center. Instead of the manual feature extraction methods typically employed in signal processing, a novel one-dimensional convolutional architecture allows the trained model to autonomously extract pertinent features directly from the raw signals. Several signal channels are utilized, including vibrations, motor speeds, and motor torques. Three quality indicators—average roughness, peak-to-valley roughness, and diameter deviation—are monitored using a single model, resulting in a compact and efficient classifier. Training data are obtained via a small number of experiments designed to induce variability in the quality metrics by varying feed, cutting speed, and depth of cut. A sliding window technique augments the dataset and allows the model to seamlessly operate over the entire process. This is further facilitated by the model’s ability to distinguish between cutting and non-cutting phases. The base model is evaluated via k-fold cross validation and achieves average F1 scores above 0.97 for all outputs. Consistent performance is exhibited by additional instances trained under various combinations of design parameters, validating the robustness of the proposed methodology.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring

Stathatos,

Tzimas,

Benardos

et al. 2024

Sensors

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“…The pull-down instability is an important phenomenon in the Toda oscillator, 26 just as the pull-in instability in the MEMS system. Unfortunately, the pull-down instability of the oscillator was not caught much attention yet though the fractional-order convolutional neural network 27,28 was now used for dealing with chatter vibration systems.…”

Section: Introductionmentioning

confidence: 99%

Pull-down plateau of a Toda-like fractal-fractional oscillator and its application in grass carp’s roes’ agglomeration

Niu

2023

Journal of Low Frequency Noise, Vibration and Active Control

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Grass carp’s roes should be agglomerated together for maximizing their survival rate against various predators. Any vibration induced by any environmental perturbation should be attenuated immediately. A Toda-like fractal-fractional oscillator is established, which shows a low-frequency property for most cases; however, the grass carp has evolved a very ability to attenuate the perturbated vibration by sticky adhesion. The pull-down stability of the roes’ vibration is discovered through the results of phase diagrams. The mathematical analysis reveals that there is a pull-down plateau for the attenuating process, the plateau’s height and width are discussed graphically, and the main factors affecting the plateau’s properties are elucidated. The paper offers a totally new window for biomechanics, especially for biomimicking design of chatter vibration systems inspired by the agglomerated roes.

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“…[16]. Fractional models for Gaussian white noise also caught much attention [17][18][19][20][21], and the fractional convolution kernel neural network is a suitable mathematical tool for fault diagnosis [22][23][24]. Duffing oscillator [25,26] is extended to its fractional partner under noise [27,28].…”

Section: Introductionmentioning

confidence: 99%

Fractional stochastic vibration system under recycling noise

Zhang

Wang

2023

Front. Phys.

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The fractional stochastic vibration system is quite different from the traditional one, and its application potential is enormous if the noise can be deployed correctly and the connection between the fractional order and the noise property is unlocked. This article uses a fractional modification of the well-known van der Pol oscillator with multiplicative and additive recycling noises as an example to study its stationary response and its stochastic bifurcation. First, based on the principle of the minimum mean square error, the fractional derivative is equivalent to a linear combination of damping and restoring forces, and the original system is simplified into an equivalent integer order system. Second, the Itô differential equations and One-dimensional Markov process are obtained according to the stochastic averaging method, using Oseledec multiplicative ergodic theorem and maximal Lyapunov exponent to judge local stability, and judging global stability is done by using the singularity theory. Lastly, the stochastic D-bifurcation behavior of the model is analyzed by using the Lyapunov exponent of the dynamical system invariant measure, and the stationary probability density function of the system is solved according to the FPK equation. The results show that the fractional order and noise property can greatly affect the system’s dynamical properties. This paper offers a profound, original, and challenging window for investigating fractional stochastic vibration systems.

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Novel fractional-order convolutional neural network based chatter diagnosis approach in turning process with chaos error mapping

Cited by 36 publications

References 34 publications

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring

Pull-down plateau of a Toda-like fractal-fractional oscillator and its application in grass carp’s roes’ agglomeration

Fractional stochastic vibration system under recycling noise

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