Tohoku University · Materials Science
Professor Daisuke Ando's research lab specializes in advanced lightweight alloys, with a focus on magnesium and aluminum-based materials for structural and energy applications. Key research directions include developing shape-memory and superelastic magnesium alloys, enhancing hydrogen generation through nanostructured Mg–Ca alloys, and improving high-temperature strength in aluminum alloys using machine learning-driven materials design. The lab integrates experimental metallurgy with advanced characterization techniques such as TEM and XRD to understand deformation mechanisms and phase transformations at the microstructural level.
Figures are computed from collected data and may differ slightly.
Shape-memory alloys (SMAs), which display shape recovery upon heating, as well as superelasticity, offer many technological advantages in various applications. Those distinctive behaviors have been observed in many polycrystalline alloy systems such as nickel titantium (TiNi)-, copper-, iron-, nickel-, cobalt-, and Ti-based alloys but not in lightweight alloys such as magnesium (Mg) and aluminum alloys. Here we present a Mg SMA showing superelasticity of 4.4% at -150°C and shape recovery upon he
The hydrogen generating characteristics of Mg–Ca alloys with Mg/Mg2Ca nanolamellar structures in the hydrolysis reaction with artificial seawater—a 3.5-wt% NaCl aqueous solution—were investigated for a new hydrogen supply source application. The concept of this study was to fabricate a complete hydrolysis reaction alloy using nanolamellar structure of Mg and Mg2Ca having an electrochemically less noble product than Mg. The hydrolysis reaction properties of Mg-10Ca, Mg-15Ca, Mg-16.2Ca, Mg-20Ca, a
In Mg alloys, twins have been known to be an important deformation mechanism. However, their roles on deformation mechanisms have not been well understood. In this work, we performed tensile test of rolled sheets of AZ31 Mg alloys at room temperature. A number of large surface reliefs were observed in a region near a fractured edge. TEM observation showed the formation of twins under the large surface relief. Crystallographic analysis indicated that the basal planes of the twins were tilted by a
The high-temperature strength of aluminum alloys must be enhanced for improving their applicability across industries. This study proposes a machine learning approach for developing heat-resistant aluminum alloys. Using a combination of correlation-based screening and genetic algorithms, feature selection was performed on descriptors derived from the atomic compositions of alloys. Then, alloy compositions and descriptors were used as input variables of the model to improve its robustness and app
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