A Thermal Displacement Prediction System With an Automatic LRGTVAC-PSO Optimized Branch Structured Bidirectional GRU Neural Network

Kuo, Ping‐Huan; Chen, Yen‐Wen; Hsieh, Tung-Hsien; Jywe, Wen-Yuh; Yau, Her‐Terng

doi:10.1109/jsen.2023.3269064

Cited by 17 publications

(2 citation statements)

References 31 publications

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“…Kuo et al [ 24 ] discuss an approach for autonomous optimization using bidirectional GRU that can accurately forecast manufacturing mistakes. The methods implement an optimized automatic logistic random generator time-varying acceleration coefficient particle swarm optimization (LRGTVAC-PSO) method to optimize a branch structured bidirectional Gated Recurrent Unit (GRU) neural network.…”

Section: Background Review and Related Workmentioning

confidence: 99%

An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools

Kristiani,

Wang,

Liu

et al. 2024

Sensors

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This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi.

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Section: Background Review and Related Workmentioning

confidence: 99%

An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools

Kristiani,

Wang,

Liu

et al. 2024

Sensors

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show abstract

“…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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Comparative analysis of new approximate analytical method and Mohand variational transform method for the solution of wave-like equations with variable coefficients

et al. 2023

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A Thermal Displacement Prediction System With an Automatic LRGTVAC-PSO Optimized Branch Structured Bidirectional GRU Neural Network

Cited by 17 publications

References 31 publications

An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools

An Intelligent Thermal Compensation System Using Edge Computing for Machine Tools

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

Comparative analysis of new approximate analytical method and Mohand variational transform method for the solution of wave-like equations with variable coefficients

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