Research

  Structural materials used in our lives are required to have mechanical functions and are produced by various manufacturing processes (casting, forging, hot-dip galvanizing, sintering, heat treatment, additive manufacturing, etc.), which is essential for developing a safe and sustainable society.

Our team explores the fundamentals of controlling micro- and nano-structures produced by various manufacturing processes for structural metallic materials. Based on the fundamentals, we design elemental compositions and production processes to create innovative structural materials with high and multi-functionality. We will also use micromechanical testing and in-situ observation to explore the nature of plastic deformation of metal crystals to improve further mechanical performance.
We would like to encourage students and young researchers to hone their abilities and develop their skills through independent research activities. We have an international membership, including international students and researchers, and actively collaborate with other universities, research institutes, and industries to keep high activity in each research.

 

How to design non-equilibrium materials created by metal additive manufacturing?

Metal additive manufacturing (often denoted as “Metal 3D Printers”) can control not only “topology" but also "property”.
Laser powder bed fusion (L-PBF) with metal powders, a type of additive manufacturing known as metal additive manufacturing, can produce components with complex three-dimensional shapes that are impossible with conventional manufacturing processes. The metal components produced by the L-PBF process are manufactured via an ultrafast solidification process (a phenomenon in which liquid metal transforms to solid at a high cooling speed above 1 million degrees per second) by scanning laser irradiation.

Our team focuses on the micro- and nano-structure produced by the L-PBF process, which is characterized not only by being very refined but also by being in a non-equilibrium state. Such a non-equilibrium state created by the L-PBF process not only dramatically improves the performance of metallic materials but also creates anomalous physical properties that are sometimes opposite to the common sense of materials science. Material design using nonequilibrium states created by metal additive manufacturing technologies can be applied not only to Al alloys, but also to various metals, and we are developing copper alloys, steels, and composite materials. Based on the fundamental understanding, we will use computational calculations to optimize chemical compositions in multi-elemental systems and produce materials with innovative high functionality and multifunctionality using metal additive manufacturing technologies.

     
 

How to design materials using impurity elements believed to be harmful?

Aluminum (Al) is produced from a raw material called "bauxite" through an electrolytic refining process, and all new Al ingots are imported from overseas due to the amount of electricity used in the domestic electrolytic refining process. On the other hand, the use of recycled Al can reduce CO₂ emissions during production by 97% compared to the use of new ingots, making efforts to promote a recycling-oriented economy an urgent necessity from a carbon-neutral perspective. A large amount of scrap aluminum alloys, which are commonplace in our daily lives, contain many impurities, such as silicon (Si) and iron (Fe). These elements are usually removed because they form intermetallic compounds with Al, making the materials brittle and degrading.

Our team aims to effectively utilize impurity elements (Fe, Si, Mn, etc.) that are considered harmful and to achieve high mechanical performance for Al alloys by using casting and heat treatments, which are the production processes of Al alloys. We will establish principles for controlling micro- and nanostructures using impurity elements by using theoretical calculations and electron microscope analysis techniques and design Al alloy component utilization and processes to realize improved recycling.

     

How to design hot-dip galvanizing coatings for steel surfaces?

As described in high school chemistry textbooks, hot-dip zinc (Zn) galvanizing surface treatment (coating) technology provides high corrosion resistance (sacrificial corrosion protection), which is an important environmental resistance property for steels. Hot-dip Zn galvanizing is applied to various structural steels and is one of the leading products in the Japanese steel industry. Therefore, the technology of the hot-dip galvanizing process (development technology of high-performance surface-treated steels) is directly related to corporate profits in the steel industry, and the research results within companies are accumulated as know-how, most of which are not open to the public, and the fundamental knowledge that can be shared is limited so far.

Our team, belonging to a university as a neutral position, conducts fundamental studies for the development of high-functional and multifunctional hot-dip galvanized coatings that are not limited to environmental resistance and builds an academic foundation for innovation in hot-dip galvanizing technology. Based on theoretical calculations, we not only understand the formation mechanism of micro- and nanostructures through the solid-liquid reaction process in the hot-dip galvanizing process but also elucidate the dominant factors contributing to mechanical functions such as workability using experimental methods combined with electron microscopy techniques.

     

Fundamentals of crystal plasticity in designed materials

A micropillar compression test is a method of investigating mechanical properties by fabricating micropillars of a few micrometers in size from a specific region in the small samples using a Focused Ion Beam (FIB) system and performing compression tests using a nanoindenter system. This technique is useful for fundamental studies on the strength and deformation of various materials because it is easy to perform compression tests on single-crystals.

In our team, we take advantage of the ability to experimentally measure the mechanical properties of specimens made from a specific region of a microscopic specimen under electron microscopy to investigate the mechanical properties of not only metal/alloys but also the constituent phases within composite or multi-phase materials in terms of crystal plasticity. In addition, since the test can be performed in a limited volume, experimental measurements of the strength and plastic deformation of surface coating materials and powder particle materials developed at various universities and research institutes in Japan and overseas are also performed. The micropillar compression test can provide insight into the deformation mechanism of novel materials.
     

Predicting microstructure evolution using the phase-field method

The phase-field method is used to predict the microstructure evolution in materials by analyzing the energy relaxation process of the microstructure. By implementing the Gibbs energy parameters evaluated in the field of calculation of phase diagrams (CALPHAD) and atomic diffusion mobility parameters in the phase-field model, it is possible to simulate the microstructure evolution of practical materials. 

Furthermore, by evaluating the elastic strain energy of the inhomogeneous microstructure generated by solid-solid phase transformation based on the microelasticity theory and adding it to the energy of the microstructure, it is possible to simulate the microstructure evolution considering the effect of the internal elastic field. Our research group has succeeded in predicting the microstructure evolution (so-called “rafting”) in which cuboidal precipitates anisotropically coarsen and change into plate-like structures during high-temperature creep in nickel-base superalloys.

We also have succeeded in predicting the microstructure evolution in which a multivariant structure consisting of multiple crystal orientation variants is formed and high-density dislocations are introduced during martensitic transformation in steels. In this way, by modeling various microstructure evolutions in structural materials based on the phase-field method, we aim to elucidate the mechanisms of microstructure evolution and propose innovative methods for controlling microstructure evolution.


   

Understanding the origin of superior mechanical properties of materials through microstructure prediction

The phase-field method can be used to analyze the relationship between material microstructure and mechanical properties (stress-strain response). For example, by modeling the martensitic transformation under external stress in superelastic/shape memory alloys, it is possible to predict the microstructure evolution corresponding to the stress-strain curve. Our research is revealing that the nanoscale microstructure prior to martensitic transformation has a significant effect on the macroscopic mechanical properties. 

It is known that the segregation of alloying elements to crystal defects (dislocation, grain boundaries, etc.) changes the mechanical properties of a material. Our research group has proposed a method to predict the equilibrium composition of grain boundaries in multicomponent alloys by using the thermodynamic database developed in the field of calculation of phase diagrams (CALPHAD). We have also analyzed the relationship between the predicted grain boundary composition and creep rupture strength of nickel-base superalloys and have demonstrated that the segregation of certain elements to grain boundaries is effective in improving the creep rupture strength.

In this way, by analyzing the relationship between the predicted microstructure and mechanical property data of materials, we aim to understand the origin of superior mechanical properties of materials, as well as to quantitatively clarify the microstructure factors that are important in improving mechanical properties of materials.


   

Efficiently estimating material parameters from microstructure data

Phase-field models and other microstructure prediction models include many material parameters, but it is rare for all of these values to be known, which is a major hurdle when performing microstructure simulations. 

Therefore, our research group is studying an approach to efficiently extract information about material parameters from material microstructure data by assimilating experimental data of microstructure with a microstructure computational model. We have demonstrated that by fusing time-series data of material microstructure changes with a phase-field model using a data assimilation method, it is possible to simultaneously estimate the Gibbs energy parameters for the calculation of phase diagrams (CALPHAD) and the atomic diffusion mobility parameters. 

We have also demonstrated that by fusing experimental data of secondary phase particle shapes with a microstructure computational model and further utilizing machine learning techniques, it is possible to efficiently estimate the interface energy and lattice mismatch between the two phases. In this way, by combining experimental data on material microstructure with a microstructure computational model and efficiently accumulating information on various material parameters, it is expected that the prediction accuracy of microstructure simulations will be improved.