{10–12} extension twinning activity and compression behavior of pure Mg and Mg-0.5Ca alloy at cryogenic temperature

Han, Gukin; Noh, Yeonju; Chaudry, Umer Masood; Park, Sung Hyuk; Hamad, Kotiba; Jun, Tea‐Sung

doi:10.1016/j.msea.2021.142189

Cited by 40 publications

(2 citation statements)

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“…It is well known that the task of improving the ductility in Mg-based materials can be done successfully by a concrete understanding of the processing-microstructure-property relationships. Indeed, a huge number of studies have been conducted to solve the ductile issue in Mg-based materials using various strategies, and those are mostly experimental works [5][6][7], with some present works done based on computational methods (DFT [8] and MD [9]). Up to now, very few studies have discussed the application of data-based methods (machine learning (ML)) to figure out the relation between the structure and ductility of Mg materials.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning Analysis of Stress Concentration in Magnesium: An Insight beyond the Black Box of Predictive Modeling

2022

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In the present work, machine learning (ML) was employed to build a model, and through it, the microstructural features (parameters) affecting the stress concentration (SC) during plastic deformation of magnesium (Mg)-based materials are determined. As a descriptor for the SC, the kernel average misorientation (KAM) was used, and starting from the microstructural features of pure Mg and AZ31 Mg alloy, as recorded using electron backscattered diffraction (EBSD), the ML model was trained and constructed using various types of ML algorithms, including Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), Naive Bayes Classifier (NBC), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), and Extremely Randomized Trees (ERT). The results show that the accuracy of the ERT-based model was higher compared to other models, and accordingly, the nine most-important features in the ERT-based model, those with a Gini impurity higher than 0.025, were extracted. The feature importance showed that the grain size is the most effective microstructural parameter for controlling the SC in Mg-based materials, and according to the relative Accumulated Local Effects (ALE) plot, calculated to show the relationship between KAM and grain size, it was found that SC occurs with a lower probability in the fine range of grain size. All findings from the ML-based model built in the present work were experimentally confirmed through EBSD observations.

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Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning Analysis of Stress Concentration in Magnesium: An Insight beyond the Black Box of Predictive Modeling

2022

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“…Calcium (Ca) is reported to be the most attractive candidate owing to low cost, adequate solid solubility and large atomic size equivalent to RE elements [19][20][21]. Han et al evaluated the compressive behavior of the Mg-0.5Ca at room and cryogenic temperature (−150 • C) [22]. The results showed that the Mg-0.5Ca showed a significant increase in the ductility at both deformation temperatures as compared to the pure Mg, which was associated with the texture softening in the Mg-0.5Ca.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Microstructure, Crystallographic Texture and Mechanical Behavior of Hot-Rolled Pure Mg and Mg-2Al-1Zn-1Ca Alloy

2022

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In this study, the microstructure, crystallographic texture and the mechanical performance of hot-rolled pure Mg and Mg-2Al-1Zn-1Ca (herein inferred as AZX211) were thoroughly investigated. The results showed that the designed AZX211 alloy exhibited an exceptional strength/ductility synergy where an almost 40% increase in ductility was received for AZX211. The microstructural characterization revealed the grain refinement in the AZX211, where the grain size was reduced by more than 50% (24.5 µm, 10 µm for the pure Mg and the AZX211, respectively). Moreover, a discernible number of precipitates were dispersed in the AZX211, which were confirmed to be (Mg, Al)2Ca. The pure Mg showed a conventional strong basal texture while a significantly weakened split basal texture was received for the AZX211. The fraction of basal-oriented grains was 21% for the pure Mg and 5% for the AZX211. The significant texture weakening for the AZX211 can be attributed to the precipitation and co-segregation that triggered the preferential evolution of the non-basal grains while impeding the growth of the basal grains. This was also confirmed by the crystal orientation and the pseudo-rocking curves. The higher ductility of the AZX211 was explained based on the texture softening and Schmid factor for the basal and non-basal slip systems.

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Comparative Study of Room and Cryogenic Deformation Behavior of Additive Manufactured Ti–6Al–4V Alloy

Chaudry,

Noh,

Lee

et al. 2024

Adv Eng Mater

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The microstructural evolution and underlaying deformation behavior of Ti–6Al–4V alloy fabricated by laser powder bed fusion (LPBF‐Ti64) during room (RT) and cryogenic (CT: −150 °C) temperature are systematically investigated. LPBF‐Ti64 exhibits significantly higher strength at CT (yield strength [YS]: 1339 MPa, ultimate compressive strength [UCS]: 1982 MPa) as compared to RT (YS: 1013 MPa, UCS: 1690 MPa) with reduction in fracture strain. To probe the deformation mechanisms responsible for exceptionally higher strength at CT, LPBF‐Ti64 alloy is compressed to 10% strain at both temperatures and the underlaying strengthening mechanisms are investigated. Dislocation strengthening is emphasized to be the major strengthening mechanisms (1166/1359 MPa for RT‐10 and CT‐10, respectively). The significantly higher dislocation density at CT (3.73 1016 m−2 for CT‐10 and 2.9 1016 m−2 for RT‐10) is attributed to the restricted dislocation movement at low temperature, whereas dislocation nucleation during further deformation will multiply the dislocations interlocking and pinning, hence resulting in higher strength.

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{10–12} extension twinning activity and compression behavior of pure Mg and Mg-0.5Ca alloy at cryogenic temperature

Cited by 40 publications

References 40 publications

Interpretable Machine Learning Analysis of Stress Concentration in Magnesium: An Insight beyond the Black Box of Predictive Modeling

Interpretable Machine Learning Analysis of Stress Concentration in Magnesium: An Insight beyond the Black Box of Predictive Modeling

Investigating the Microstructure, Crystallographic Texture and Mechanical Behavior of Hot-Rolled Pure Mg and Mg-2Al-1Zn-1Ca Alloy

Comparative Study of Room and Cryogenic Deformation Behavior of Additive Manufactured Ti–6Al–4V Alloy

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