Comparison study of ductile iron produced with Martian regolith harvested iron from ionic liquids and Bosch byproduct carbon for in-situ resource utilization versus commercially available 65-45-12 ductile iron

Stewart, Blake; Doude, Haley; Mujahid, Shiraz; Abney, Morgan B.; Fox, Eric T.; Edmunson, Jennifer; Mehan, Jeffrey J.; Henry, Christopher R.; Hall, Phil; Kadiri, Haitham El; Rhee, Hongjoo

doi:10.1016/j.asr.2022.10.060

“…By adjusting the content of alloying elements or adjusting the cooling rate to adjust the matrix organization and graphite morphology of heavy-section ductile iron, heavy-section ductile iron castings with high performance can be obtained [15] . However, preparing heavy-section ductile iron is costly and time-consuming, and the chemical compositions of its main constituents interact with each other, which seriously restricts the application of high fracture toughness heavy-section ductile iron [16,17] . This paper adopts a physical simulation method and homemade physical simulation casting system.…”

Section: Structure Of This Thesismentioning

confidence: 99%

Prediction of heavy-section ductile iron fracture toughness based on machine learning

Song,

Zhang,

Zhang

et al. 2023

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The preparation process and composition design of heavy-section ductile iron are the key factors affecting its fracture toughness. These factors are challenging to address due to the long casting cycle, high cost and complex influencing factors of this type of iron. In this paper, 18 cubic physical simulation test blocks with 400 mm wall thickness were prepared by adjusting the C, Si and Mn contents in heavy-section ductile iron using a homemade physical simulation casting system. Four locations with different cooling rates were selected for each specimen, and 72 specimens with different compositions and cooling times of the heavy-section ductile iron were prepared. Six machine learning-based heavy-section ductile iron fracture toughness predictive models were constructed based on measured data with the C content, Si content, Mn content and cooling rate as input data and the fracture toughness as the output data. The experimental results showed that the constructed bagging model has high accuracy in predicting the fracture toughness of heavy-section ductile iron, with a coefficient of coefficient (R2) of 0.9990 and a root mean square error (RMSE) of 0.2373. Therefore, the design requirements of high fracture toughness heavy-section ductile iron, such as nuclear spent fuel storage and transportation containers, wind power generation bases and high-speed railroads, are satisfied.

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“…By adjusting the content of alloying elements or adjusting the cooling rate to adjust the matrix organization and graphite morphology of heavy-section ductile iron, heavy-section ductile iron castings with high performance can be obtained 17 . However, preparing heavy-section ductile iron is costly and time-consuming, and the chemical compositions of its main constituents interact with each other, which seriously restricts the application of high fracture toughness heavy-section ductile iron 18 , 19 . In the preliminary work, the project team and Qiqihar No.1 Machine Tool Factory have prepared 20 BQH-20 spent fuel transport containers, and A series of preparation process articles were published, the National Natural Science Foundation for large nuclear spent fuel storage and transportation container was applied 20 – 22 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of heavy-section ductile iron fracture toughness based on machine learning

Song,

Zhang,

Zhang

et al. 2024

Sci Rep

0

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The preparation process and composition design of heavy-section ductile iron are the key factors affecting its fracture toughness. These factors are challenging to address due to the long casting cycle, high cost and complex influencing factors of this type of iron. In this paper, 18 cubic physical simulation test blocks with 400 mm wall thickness were prepared by adjusting the C, Si and Mn contents in heavy-section ductile iron using a homemade physical simulation casting system. Four locations with different cooling rates were selected for each specimen, and 72 specimens with different compositions and cooling times of the heavy-section ductile iron were prepared. Six machine learning-based heavy-section ductile iron fracture toughness predictive models were constructed based on measured data with the C content, Si content, Mn content and cooling rate as input data and the fracture toughness as the output data. The experimental results showed that the constructed bagging model has high accuracy in predicting the fracture toughness of heavy-section ductile iron, with a coefficient of coefficient (R2) of 0.9990 and a root mean square error (RMSE) of 0.2373.

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Metallurgical Investigation of Buried Ductile Cast Iron Sewerage System Pipeline Premature Failure

Hassan

2023

J Fail. Anal. and Preven.

0

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Comparison study of ductile iron produced with Martian regolith harvested iron from ionic liquids and Bosch byproduct carbon for in-situ resource utilization versus commercially available 65-45-12 ductile iron

Cited by 4 publications

References 12 publications

Prediction of heavy-section ductile iron fracture toughness based on machine learning

Prediction of heavy-section ductile iron fracture toughness based on machine learning

Prediction of heavy-section ductile iron fracture toughness based on machine learning

Metallurgical Investigation of Buried Ductile Cast Iron Sewerage System Pipeline Premature Failure

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