Application of neural network in micromechanical deformation behaviors of Inconel 740H alloy

Wang, Xiaojun; Liu, Yu; Song, Yaohui; Li, Huaying; Hu, Xiao; Ji, Yafeng

doi:10.1007/s00170-023-10908-1

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…More importantly, during the rolling process, the microstructure undergoes many physical and chemical changes, leading to the coexistence of multiple metallographic phases and changes in mechanical properties. Wang et al [11] have developed a framework utilizing machine learning and rate-dependent crystal plasticity finite elements to predict the flow stress and texture evolution of Inconel 740H alloy under uniaxial compression. Li et al [12] have proposed a machine learning method guided by physical metallurgy principles to rapidly and accurately predict the thermal deformation mechanisms of antibacterial stainless steels with varying copper (Cu) contents.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Analysis of Hot Rolling Strip Tension Based on Back Propagation Neural Network

Yuan,

Ma,

et al. 2024

Preprint

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In modern hot strip mill control systems, strip tension control is the core control function, and its performance will be directly reflected in product quality. A strip tension prediction model based on Back Propagation (BP) neural network is proposed. To ensure that the true tension value is obtained, this paper proposes a four-dimensional judgment mode for the contact time between the looper and the strip steel and establishes a data set of tension parameters for hot rolled strip steel. The traditional BP neural network, genetic algorithm optimized BP neural network (GA-BP), and whale algorithm optimized BP neural network (WOA-BP) models were used to predict the strip tension, and their prediction performance was evaluated. The results show that the proposed WOA-BP model has the best prediction effect, with the highest model decision coefficient of 0.9330. At the same time, the contribution rate of each variable to the strip tension was studied, and the results showed that the looper angle and looper roller force had the greatest impact on it, consistent with physical laws. Propose improvement suggestions for the control performance of hot rolled strip tension based on the influence of looper angle on the prediction model.

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Section: Introductionmentioning

confidence: 99%

Prediction and Analysis of Hot Rolling Strip Tension Based on Back Propagation Neural Network

Yuan,

Ma,

et al. 2024

Preprint

Self Cite

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“…Alloy 740H is known to be one of the candidate boiler materials available for steam temperatures higher than 700 • C in thermal power plants. It was developed by increasing the Al content and limiting the contents of Nb, Ti, and B to inhibit the formation and growth of the G phase ((Nb,Ti) 6 (Ni,Co) 16 Si 7 ) based on alloy 740 [8][9][10]. Alloy 740H has high creep strength due to precipitation strengthening by the gamma prime (γ ) phase, which is an intermetallic compound with an L12 ordered face-centered cubic structure with composition Ni 3 (Al,Ti) [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Effect of Ti/Al Ratio on Precipitation Behavior during Aging of Ni-Cr-Co-Based Superalloys

Chu,

Park,

Lee

et al. 2023

Metals

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Precipitation behaviors of Ni-Cr-Co-based superalloys with different Ti/Al ratios aged at 750, 800, and 850 °C for up to 10,000 h were investigated using scanning and transmission electron microscopy. The Ti/Al ratio did not significantly affect the diameter of the γ′ phase. However, the volume fraction of the γ′ phase increased with increasing Ti/Al ratios. The η phase was not observed in alloys with a small Ti/Al ratio, whereas it was precipitated after aging at 850 °C for 1000 h in alloys with a Ti/Al ratio greater than 0.80. Higher aging temperatures and higher Ti/Al ratios led to faster η formation kinetics and accelerated the degradation of alloys. It is thought that the increase in hardness with an increase in the Ti/Al ratio is attributed to the effective inhibition of the γ′ phase on dislocation movement due to the increase in the volume fraction of the γ′ phase and an increase in the antiphase boundary (APB) energy.

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Tensile properties and deformation characteristics of nickel-iron-based superalloys for steam turbine rotors

Huang,

Yan,

Lei

et al. 2024

emergent mater.

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Application of neural network in micromechanical deformation behaviors of Inconel 740H alloy

Cited by 4 publications

References 28 publications

Prediction and Analysis of Hot Rolling Strip Tension Based on Back Propagation Neural Network

Prediction and Analysis of Hot Rolling Strip Tension Based on Back Propagation Neural Network

Effect of Ti/Al Ratio on Precipitation Behavior during Aging of Ni-Cr-Co-Based Superalloys

Tensile properties and deformation characteristics of nickel-iron-based superalloys for steam turbine rotors

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