Session Details
[S8]S8 Multi-scale analysis of elementary processes in plasticity (VIII)(2)
Fri. Sep 19, 2025 9:00 AM - 12:20 PM JST
Fri. Sep 19, 2025 12:00 AM - 3:20 AM UTC
Fri. Sep 19, 2025 12:00 AM - 3:20 AM UTC
Room L(C308 3rd floor Building C)
Chair:Ryosuke Matsumoto
※表示の講演時間には質疑応答時間も含みます。
(質疑応答時間5分、基調講演と招待講演は5~10分)
(質疑応答時間5分、基調講演と招待講演は5~10分)
[S8.18][Keynote Lecture] Evaluation of Local Strain Concentration in Polycrystalline Materials Using SEM-DIC and Crystal Plasticity Simulation
*SADAMATSU Sunao1 (1. Department of Mechanical Engineering, Faculty of Engineering, Kagoshima University)
[S8.19]Evaluation of slip persistence in a polycrystalline Ti-22V
*Yano Rei1, Morikawa Tatsuya2, Yamasaki Shigeto2, Tsuru Tomohito3, Tanaka Masaki2 (1. 九大工(院生)、2. 九大工、3. JAEA)
[S8.20]Atomistic analysis of the effect of solute concentration fluctuations on plastic deformation in β-type Ti-Nb alloys
*Mitsuhara Akihiro1, Kimizuka Hajime2 (1. Nagoya Univ. (D3), 2. Nagoya Univ.)
[S8.21]Effect of Deformation Incompatibility on Work Hardening in Polycrystalline α titanium
*KAWANO Yoshiki1, MITSUHARA Masatoshi2, MAYAMA Tsuyoshi3 (1. Kitami Inst. Technol., 2. Kyushu Univ., 3. Kumamoto Univ.)
break
[S8.22][Keynote Lecture] Crystal Plasticity Analysis of Microstructure-Dependent Critical Resolved Shear Stress and Plastic Slip Deformation Behavior
*OKUYAMA Yelm1, OHASHI Tetsuya2 (1. NIT Kisarazu Coll., 2. Emeritus professor Kitami Inst. of Technol.)
[S8.23]DFT analysis of formation behaviors of cluster-arranged layers in Mg-Y-Zn alloy
*MATSUMOTO Ryousuke1, UEMURA Naoki1 (1. Kyoto University of Advanced Science)
[S8.24]Slip-System-Dependence in Dislocation Strengthening:A Molecular Dynamics Study
*Wakamura Urara1, Niiyama Tomoaki2, Shimokawa Tomotsugu3 (1. Graduate School of Natural Science and Technology, Kanazawa Univ., 2. Institute of Science and Engineering, Kanazawa Univ., 3. Institute of Science and Engineering, Kanazawa Univ.)
[S8.25]Crystal Structure Prediction and Stacking Fault Energies in Non-Basal Slip Systems of Mg-Y Alloys Using Machine Learning Potential
*KOSAKAMOTO EIKI1, SHINJI ANDO2, TSUMURAYA TAKAO2 (1. Grad. Sch. Sci. Tech. Kumamoto Univ., 2. Magnesium Research Center, Kumamoto Univ.)