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Oral presentation

Actual stress analysis of welded part using quantum beam hybrid method

Suzuki, Kenji*; Miura, Yasufumi*; Shiro, Ayumi*; Toyokawa, Hidenori*; Saji, Choji*; Morooka, Satoshi; Shobu, Takahisa

no journal, , 

Oral presentation

Pearlite growth kinetics in Fe-C-Mn eutectoid steels

Zhang, Y.*; Umeda, Takemasa*; Morooka, Satoshi; Miyamoto, Goro*; Furuhara, Tadashi*

no journal, , 

Oral presentation

DFT-based description of dislocation core and dynamics in BCC HEAs

Tsuru, Tomohito; Lobzenko, I.

no journal, , 

High-entropy alloys (HEAs) have been receiving increased attention for their excellent mechanical properties. Especially, some body-centered cubic (BCC) HEAs exhibit excellent mechanical properties which shows a good balance between high strength and high elongation. In addition, BCC-HEAs containing certain elements cause peculiar slip traces. However, the mechanisms of these characteristics are not fully understood. To elucidate the mechanism of the excellent mechanical properties of HEA, it is essential to understand the atomic-level depiction of the defect structure, taking into account the influence of the constituent elements. Dislocation structure and dislocation motion should be key to characterizing the mechanical properties of HEA. It is worthwhile to consider the differences between two types of BCC-HEA, VNbMoTaW and TiZrNbHfTa. We believe that the origin of the significant lattice distortion and the peculiar dislocation core structure lies in the presence of sufficient concentrations of group IV elements. In this study, we investigated the dislocation core structure in BCC-HEAs using first-principles calculations. The results show that the mean square atomic displacement of TiZrNbHfTa is very large. We also developed a new method to capture the dynamics of dislocations in HEAs.

Oral presentation

Atomic stress distribution near Al surfaces, calculated using artificial neural network interatomic potential

Lobzenko, I.; Shiihara, Yoshinori*; Mori, Hideki*; Matsunaka, Daisuke*; Tsuru, Tomohito

no journal, , 

Recently methods of machine learning have become an important part of materials science. Particularly, interatomic potentials built using such methods demonstrate accuracy of geometrical characteristics of materials approaching the accuracy of first-principle calculations. In our work we use artificial neural networks to build potentials, and therefore they are called ANN potentials. We focus on the atomic stress, one of the important properties of materials, which can be calculated in classical approximation. Analysis of microscopic stress can be applied to any type of nonuniform system (such as defects in bulk, two-dimensional crystals, molecules assemblies, etc.). We show how the central-force decomposition (CFD) scheme can be used in the framework of ANN potentials for the derivation of atomic stress tensor. It is important to use CFD due to the fact that the symmetry of the stress tensor may be broken in other schemes. Finally, we calculate atomic stress distributions near surfaces of pure Al with different orientations. It is known from first-principle studies that there is a charge oscillations near Al surfaces, however it cannot be captured by existing interatomic potentials. Our results, obtained with the new ANN potential, show oscillations of atomic stress near Al surface. Even though our potential was fitted to only energies of Al structures (calculated in quantum-mechanics approximation), we attribute the atomic stress oscillations to the charge distribution of the real system. Charge oscillations are affecting total energies of structures, and therefore are implicitly included in the data set, which we use for building the potential.

Oral presentation

Cyclic tension-compression in situ neutron diffraction of extruded LPSO typed magnesium alloys

Harjo, S.; Gong, W.; Kawasaki, Takuro; Aizawa, Kazuya; Yamasaki, Michiaki*

no journal, , 

Oral presentation

Cryogenic deformation behavior of advanced alloys monitored by neutron diffraction

Harjo, S.; Kawasaki, Takuro; Gong, W.; Mao, W.

no journal, , 

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