Numerical simulation of the casting process of a wind turbine rotor hub

Yeh, Chun-Ping; Hwang, Weng-Sing

doi:10.1002/we.393

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…The simulation results showed that particle clustering/ accumulation was caused by particle pushing, and the particles were distributed more uniformly as the pouring temperature increased. Yeh et al [7] performed a numerical simulation of the mold filling and solidification processes and predicted the occurrence of relative casting defects in rotor hub casting using an integrated numerical model. Then, to alleviate the casting defects in the rotor hub casting, a better alloy design was proposed based on the simulated results to alleviate casting defects of the rotor hub casting.…”

Section: Introductionmentioning

confidence: 99%

Numerical simulation of the mold filling process of the brake lever for an electric multiple unit

Cao,

2023

Mater. Res. Express

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The brake lever for an electric multiple unit (EMU) of a train is an important supporting component that directly affects the usability and safety of EMU. In this study, the mold filling, solidification, and shrinkage processes of the brake lever of the EMU were simulated using Anycasting software, and the distributions of the mold filling speed, mold filling temperature, and shrinkage defects were analyzed. The results show that the brake lever for the EMU exhibits casting defects such as shrinkage porosity and shrinkage cavities. To eliminate these casting defects, a chilled-iron process was developed; however, the appearance of cementite increased the brittleness of the castings, which deteriorated their strength and plastic toughness. Hence, a snap-chilling sheet process was designed to fabricate the brake lever for the EMU, and a numerical simulation of the process was performed using the Anycasting software. The shrinkage defects of the castings were effectively eliminated; simultaneously, the cementite disappeared, and the microstructure of the thick parts of the brake lever showed no obvious changes. The prepared brake lever satisfied the requirements of the EMU.

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Section: Introductionmentioning

confidence: 99%

Numerical simulation of the mold filling process of the brake lever for an electric multiple unit

Cao,

2023

Mater. Res. Express

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“…In recent years, with the rapid development of infrastructure construction, the demand for ductile iron parts in wind power, high-speed railways, engineering machinery and other industries has gradually increased. These castings work in the harsh environment and require high comprehensive performance of ductile iron, which requires high strength and good plasticity [8,9]. Therefore, improving the tensile strength, yield strength, hardness and elongation of ductile iron at room temperature is of great significance to engineering practice.…”

Section: Introductionmentioning

confidence: 99%

Mechanical properties prediction of ferrite ductile iron via machine learning

Gu,

Yao,

et al. 2023

Phys. Scr.

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The rapid development of the wind power industry requires ductile iron to maintain high toughness while improving strength. Machine learning offers the possibility to accelerate various aspects of material development and performance optimization. In this paper, we established a composition-property dataset for ferritic ductile iron, and a variety of machine learning algorithms are compared to construct a composition-property model for ferritic ductile iron finally. The composition is chosen as the model input, and the outputs are four properties: tensile strength, Brinell hardness, yield strength and elongation. The correlation coefficients of the models constructed are above 0.96, and the mean absolute percentage errors are below 5%. The mean relative error between the model predictions and the experimental values is 4.43%, which effectively verifies the reliability of the composition-properties model of ductile iron constructed. This paper also uses the machine learning model to predict the effect law of each element content on the mechanical properties of ductile iron, and uses thermodynamic prediction of phase composition to verify the reliability of the machine learning method to predict the mechanical properties of ductile iron, which provides strong guidance for the design of new ductile iron with high strength and high plasticity at room temperature.

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