The University of Tokyo · Engineering
Professor Junya Inoue's research lab specializes in computational materials science and metallurgy, focusing on the design and development of advanced multi-principal element alloys (MPEAs) and intermetallic compounds through machine learning and thermomechanical processing. The lab integrates machine learning models—particularly Generalized Regression Neural Networks (GRNN)—with experimental validation to predict and optimize mechanical properties such as microhardness and wear resistance. Research also extends to understanding deformation mechanisms in soft materials and interfacial fracture toughness in Fe-Zn intermetallic systems, combining experimental testing with numerical simulations. The overarching goal is to accelerate the discovery of high-performance structural materials with tailored properties for engineering applications.
Figures are computed from collected data and may differ slightly.
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
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