The University of Tokyo · 공학
Junya Inoue 교수의 연구실은 첨단 금속 상재료, 특히 다핵소성금속합금(MPEAs)과 철- zinc 간섭금속화합물(Fe-Zn IMC)의 형성 거동 및 기계적 거동을 중심으로 연구를 진행하고 있습니다. 기계적 성질 예측을 위한 기계학습 기반 설계 기법을 도입하여 초고경도 및 뛰어난 마모 내성의 신소재를 개발하고 있으며, 이는 실용적 응용에 기여할 잠재력을 지닙니다. 또한, 연약한 암석 내 응력 국중화 거동에 대한 실험 및 수치 해석을 통해 재료의 거동 메커니즘을 규명하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
The Fe-Zn intermetallic compounds (IMC) layers composed of δp, δk, and Γ phases were fabricated using two different kinds of Fe/Zn diffusion couple (DC), and the fracture toughness of the constituent phases was estimated from the toughness of the IMC layers. In the DCs with sufficient Zn supply, the IMC layers were mainly composed of δp phase after isothermal holding at 450-600 °C for 60 s, while in the DCs with limited Zn supply, the IMC layers were composed of δp, δk, and Γ phases at the early
Abstract Strain localization developing inside soft rock specimens is examined through experimental observation and numerical simulation. In the experimental study, soft rock specimens are sheared at different strain rates under plane strain conditions and deformation and strain localization characteristics are analysed. Transition of localization mode from highly localized mode for higher strain rate to distributed and diffused mode of strain localization for lower strain rates was observed. In
In this work, machine learning (ML) technique was used to discovery new multi-principal elements alloys (MPEAs) with desirable properties. Generalized Regression Neural Network (GRNN) showed high accuracy to construct the composition-microhardness model and was used for microhardness prediction and composition optimization. Based on ML results, Fe0.6Ni0.7CrAl MPEAs were designed and prepared. The proposed GRNN model aligns well with experimental data, Fe0.6Ni0.7CrAl MPEAs exhibit ultra-high micr
