Tensile and High-Cycle Fatigue Properties of Die-Cast SEN6 Magnesium Alloy

Kim, Jung Eun; Kim, Ye Jin; Bae, Jun Ho; You, Bong Sun; Park, Sung Hyuk

doi:10.1007/s12540-023-01473-z

Met. Mater. Int.

2023

DOI: 10.1007/s12540-023-01473-z

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Tensile and High-Cycle Fatigue Properties of Die-Cast SEN6 Magnesium Alloy

Jung Eun Kim¹,

Ye Jin Kim²,

Jun Ho Bae³

et al.

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2024

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Cited by 8 publications

References 42 publications

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Improved Prediction for Nonlinear Thermal History of Mg Alloy Subjected to Multiple Electropulses

Yu,

Cheon,

Lee

et al. 2024

Adv Eng Mater

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The repeated rapid heating and nonlinear cooling associated with electropulsing treatment (EPT) hinder the accurate modeling of temperature variations based on a constitutive approach using physical models. This objective is not attainable through conventional machine learning (ML) approaches as well, such as artificial neural network (ANN) and long short‐term memory (LSTM). The present study investigates the intricate nonlinear thermal history of Mg alloys induced by EPT to improve a predictive model under multipulse conditions. Furthermore, it enhances the predictivity and generalizability of a given problem using an ML model called i‐LSTM by specifically tailoring a dataset. The i‐LSTM model performance surpasses that of the constitutive approaches and conventional ML models when predicting the complicated thermal history of EPT specimens in a wide variety of processing parameters. The mechanisms underlying this improvement are discussed based on several experimental datasets. This study not only addresses challenges in existing models but also offers a promising avenue for understanding and predicting the thermal dynamics of Mg alloys subjected to the multipulse EPT process.

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Improved Prediction for Nonlinear Thermal History of Mg Alloy Subjected to Multiple Electropulses

Yu,

Cheon,

Lee

et al. 2024

Adv Eng Mater

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Interpretable Machine Learning Method for Modelling Fatigue Short Crack Growth Behaviour

Zhou,

Yang,

Xiao

et al. 2024

Met. Mater. Int.

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Tensile and Fatigue Strength, Fatigue Crack Propagation Rate, and Fracture Behavior of CaO-Added AM30 Alloy

Baek,

Shah,

Kim

et al. 2024

Met. Mater. Int.

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Tensile and High-Cycle Fatigue Properties of Die-Cast SEN6 Magnesium Alloy

Cited by 8 publications

References 42 publications

Improved Prediction for Nonlinear Thermal History of Mg Alloy Subjected to Multiple Electropulses

Improved Prediction for Nonlinear Thermal History of Mg Alloy Subjected to Multiple Electropulses

Interpretable Machine Learning Method for Modelling Fatigue Short Crack Growth Behaviour

Tensile and Fatigue Strength, Fatigue Crack Propagation Rate, and Fracture Behavior of CaO-Added AM30 Alloy

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