Research on hot deformation behavior of Zr-4 alloy based on PSO-BP artificial neural network

Wan, Peng; Zou, Hui; Wang, Kelu; Zhao, Zhengzhi

doi:10.1016/j.jallcom.2020.154047

Cited by 58 publications

(22 citation statements)

References 16 publications

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“…Table 4 shows the prediction results according to a total of 54 cases in which the number of layers and nodes were different. For an accurate comparison, the absolute error (AE) and average absolute relative error (AARE) for data points of strain from 0.1 to 0.8 with increment 0.1 at all deformation conditions are calculated as follows [28].…”

Section: Results Of Ann Modelmentioning

confidence: 99%

Predicting High Temperature Flow Stress of Nickel Alloy A230 Based on an Artificial Neural Network

Moon

Jeong

Lee

et al. 2022

Metals

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The high-temperature deformation behavior of metals and alloys undergoes complex mechanisms depending on the deformation conditions. The microstructure and mechanical properties after deformation are important factors that determine the strength and durability of the final product. Therefore, many studies to predict the microstructure and mechanical properties have been conducted. In this regard, numerous mathematical approaches for predicting microstructure and flow stress have been proposed over the past half century. Accordingly, many advances have been made in the field of material science. Nevertheless, there are limitations in the mathematical modeling method as there is a complex relationship between the deformation conditions and the mechanical properties. Therefore, in this study, flow stress prediction was performed by applying conventional constitutive equation and artificial intelligence technology, which is known to be effective in modeling complex relationships. As a result, it was confirmed that the flow stresses modeled by the artificial neural network showed a higher accuracy than the flow stresses modeled by the conventional Arrhenius hyperbolic sine equation.

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Section: Results Of Ann Modelmentioning

confidence: 99%

Predicting High Temperature Flow Stress of Nickel Alloy A230 Based on an Artificial Neural Network

Moon

Jeong

Lee

et al. 2022

Metals

View full text Add to dashboard Cite

show abstract

“…In the literature of Wang et al (2019), the weight and threshold of Elman are optimized by GA, which reduces the instability caused by the randomness of Elman initial value. Wan et al (2020) used PSO to optimize the weight and skewness of BP, and finally predicted the temperature, strain rate and tension respectively, and obtained good prediction results.…”

Section: Gamentioning

confidence: 99%

Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction

Zhang

Chen

2021

Environ Sci Pollut Res

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In recent years, a series of environmental problems have come one after another under the use of traditional fossil energy, such as greenhouse effect, acid rain, haze and so on. In order to solve the environmental problems and achieve sustainable development, seeking alternative resources has become the direction of joint efforts of China and the world. As an important part of new energy, wind energy needs strong wind speed prediction support in terms of providing stable electric power. As a result, it is very important to improve the accuracy of wind speed prediction. In view of this, this paper proposes a signal processing method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with singular value decomposition (SVD), and uses Elman neural network optimized by particle swarm optimization algorithm (PSO) and autoregressive integrated moving average model (ARIMA) to predict the intrinsic mode functions (IMFs). Firstly, CEEMDAN combined with SVD is used to decompose and denoise the data, and the weights and thresholds of Elman are optimized by PSO. Finally, the optimized Elman and ARIMA are used to respectively predict the processed wind speed data components, and then the final prediction results are obtained. The final prediction results show that the proposed model can improve the effect of wind speed prediction, reduce the prediction error, and provide strong support for the stable operation of wind farms and the grid connection of power plants.

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“…In addition, the artificial neural network model has a large parallel processing structure, which can be used to depict the complex flow behavior of materials through a variety of nonlinear relationships. [ 36–42 ]…”

Section: Introductionmentioning

confidence: 99%

Dynamic Softening Mechanism and an Improved Unified Constitutive Model for an Al–Cu–Mn–Fe–Zr Alloy during Warm Deformation

et al. 2021

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Deformation behavior of an Al–Cu–Mn–Fe–Zr alloy is investigated by plane strain compression tests at a warm deformation region. The flow stress first increases and then keeps steady, and the flow stress increases with reducing temperature or raising strain rate. However, the influence of strain rate on flow stress is weak at 100 and 150 °C. The dynamic recovery (DRV) mechanism is the dominant mechanism to balance the work hardening, and a larger number of dislocations are consumed at low strain rates. So, the deformed grains are difficult to reach the critical strain for dynamic recrystallization (DRX). When the strain rate is relatively high, the critical strain can be reached in a short time, which promotes the process of DRX. In addition, an improved unified constitutive model is built based on the evolution of dislocation density. The predicted flow stresses are in a close agreement with the measured results, proving that the built model can nicely reproduce the flow behavior.

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Research on hot deformation behavior of Zr-4 alloy based on PSO-BP artificial neural network

Cited by 58 publications

References 16 publications

Predicting High Temperature Flow Stress of Nickel Alloy A230 Based on an Artificial Neural Network

Predicting High Temperature Flow Stress of Nickel Alloy A230 Based on an Artificial Neural Network

Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction

Dynamic Softening Mechanism and an Improved Unified Constitutive Model for an Al–Cu–Mn–Fe–Zr Alloy during Warm Deformation

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