Design and Analysis of Optimal Recipe Prediction Model Based on Deep Learning for Advanced Composite Material Injection Molding

Mun, Jung-Min; Jeong, Jongpil

doi:10.1109/ccai50917.2021.9447478

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…This study uses the traditional MSE to evaluate the similarity between the target values of the test set for the PIN diode's limiter performance and the network predictions. The expression is shown as formula (6), which represents the sum of the squares of the differences between prediction values and target values. Figure 12 shows the MSE loss of the simulated-predicted values for maximum power threshold, insertion loss, and maximum isolation respectively.…”

Section: Prediction Results Of Pin Diode's Limiter Performancementioning

confidence: 99%

“…Tao Ni, Bo Guo, Can Yang, and others have utilized neural networks to design an Ultrasonic testing method for composite material bonding structures, this experiment realized the automatic ultrasonic inspection of the bonding structure [5]. Jungmin Mun1, Jongpil Jeong, and others employed neural network algorithms to establish an optimization model for deriving an optimal injection molding method for advanced composite materials, which improved formula management in the injection molding manufacturing process [6]. Neural networks are extensively used in the field of microelectronic manufacturing process, Qianhuang Chen, Tianyang Shao, Yan Xing, and others have used neural networks to predict the relationship between varied doses and energies versus substrate damage during the process of nanostructure fabrication using the helium focused ion beam (He-FIB) technique [7].…”

Section: Introductionmentioning

confidence: 99%

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A study on the high power microwave effects of PIN diode limiter based on deep learning algorithm

Chen,

Gao,

Zhao

et al. 2024

Nanotechnology

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PIN diodes, due to their simple structure and variable resistance characteristics under high-frequency high-power excitation, are often used in radar front-end as limiters to filter High Power Microwaves (HPM) to prevent its power from entering the internal circuit and causing damage. This paper carries out theoretical derivation and research on the high power microwave (HPM) effects of PIN diodes, and then uses an optimized neural network algorithm to replace traditional physical modeling to calculate and predict two types of HPM limiting indicators of PIN diode limiters. We proposes a neural network model for each of the following two prediction scenarios: In the scenario of time-junction temperature curves under different HPM irradiation, the weighted mean squared error (MSE) between the predicted values from the test dataset and the simulated values is below 0.004. While in predicting PIN limiter's power limitation threshold, insertion loss, and maximum isolation under different HPM irradiation, the MSE of the test set prediction values and simulation values are all less than 0.03. The method proposed in this research, which applies an optimized neural network algorithm to replace traditional physical modeling algorithms for studying the high-power microwave effects of PIN diode limiters, significantly improves the computational and simulation speed, reduces the calculation cost, and provides a new method for studying the high-power microwave effects of PIN diode limiters.

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Section: Prediction Results Of Pin Diode's Limiter Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A study on the high power microwave effects of PIN diode limiter based on deep learning algorithm

Chen,

Gao,

Zhao

et al. 2024

Nanotechnology

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“…In recent years, deep learning has been widely applied in the field of electronic design automation because deep learning can form more abstract high-level attribute features by combining low-level features and discovering distributed features of data [ 12 , 13 , 14 , 15 , 16 ]. Moreover, deep learning has the characteristics of simple operation and fast simulation speed, making it a very powerful research method in quantum mechanics, optical materials, nanostructures, and other fields.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning

Zhao

Wang

et al. 2023

Micromachines

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Single-event effects (SEE) are an important index of radiation resistance for fully depleted silicon on insulator (FDSOI) devices. The research into traditional FDSOI devices is based on simulation software, which is time consuming, requires a large amount of calculation, and has complex operations. In this paper, a prediction method for the SEE of FDSOI devices based on deep learning is proposed. The characterization parameters of SEE can be obtained quickly and accurately by inputting different particle incident conditions. The goodness of fit of the network curve for predicting drain transient current pulses can reach 0.996, and the accuracy of predicting the peak value of drain transient current and total collected charge can reach 94.00% and 96.95%, respectively. Compared with TCAD Sentaurus software, the simulation speed is increased by 5.10 × 102 and 1.38 × 103 times, respectively. This method can significantly reduce the computational cost, improve the simulation speed, and provide a new feasible method for the study of the single-event effect in FDSOI devices.

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Design and Analysis of Optimal Recipe Prediction Model Based on Deep Learning for Advanced Composite Material Injection Molding

Cited by 2 publications

References 8 publications

A study on the high power microwave effects of PIN diode limiter based on deep learning algorithm

A study on the high power microwave effects of PIN diode limiter based on deep learning algorithm

Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning

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