Akinori Yamanaka
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View article: Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors
Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors Open
In this review, we present a new set of machine learning-based materials research methodologies for polycrystalline materials developed through the Core Research for Evolutionary Science and Technology project of the Japan Science and Tech…
View article: High-fidelity phase-field simulation of solid-state sintering enabled by Bayesian data assimilation using in situ electron tomography data
High-fidelity phase-field simulation of solid-state sintering enabled by Bayesian data assimilation using in situ electron tomography data Open
Experimental observation methods for understanding industrially important solid-state sintering are essential for the development of new materials and devices. To experimentally characterize solid-state sintering, the limitations posed by …
View article: Inverse Estimation of Material Model Parameters Using Digital Image Correlation and Ensemble-Based Four-Dimensional Variational Methods
Inverse Estimation of Material Model Parameters Using Digital Image Correlation and Ensemble-Based Four-Dimensional Variational Methods Open
The prediction accuracy of the deformation behavior of materials by finite element (FE) simulation depends on the parameters in selected material models. Although the parameters are conventionally identified from standard material tests (e…
View article: Inverse estimation of material model parameters using Bayesian data assimilation
Inverse estimation of material model parameters using Bayesian data assimilation Open
This study proposes a new method for the inverse estimation of the parameters included in material models from full-field measurement data that are obtained using the digital image correlation method. This approach is based on data assimil…
View article: Data assimilation for phase-field simulations of the formation of eutectic alloy microstructures
Data assimilation for phase-field simulations of the formation of eutectic alloy microstructures Open
The phase-field (PF) method can effectively predict the formation of microstructures of eutectic alloys. However, numerous simulation parameters must be determined correctly for each alloy system to reproduce the experimentally observed mi…
View article: Preface to the Special Issue on “Martensitic and Bainitic Transformations in Steels; Fundamentals and Their Applications”
Preface to the Special Issue on “Martensitic and Bainitic Transformations in Steels; Fundamentals and Their Applications” Open
Martensitic and Bainitic Transformations in Steels; Fundamentals and Their ApplicationsMore than 100 years have passed since the study of martensite and bainite began in metallurgy.These microstructures continue to play a crucial role in t…
View article: Morphological evolution of splashing drop revealed by interpretation of explainable artificial intelligence
Morphological evolution of splashing drop revealed by interpretation of explainable artificial intelligence Open
This study reveals the morphological evolution of a splashing drop by a newly proposed feature extraction method, and a subsequent interpretation of the classification of splashing and non-splashing drops performed by an explainable artifi…
View article: Correlation between morphological evolution of splashing drop and exerted impact force revealed by interpretation of explainable artificial intelligence
Correlation between morphological evolution of splashing drop and exerted impact force revealed by interpretation of explainable artificial intelligence Open
This study reveals a possible correlation between splashing morphology and the normalized impact force exerted by an impacting drop on a solid surface. This finding is obtained from a newly proposed feature extraction method and a subseque…
View article: Application of Bayesian optimization to the synthesis process of BaFe2(As,P)2 polycrystalline bulk superconducting materials
Application of Bayesian optimization to the synthesis process of BaFe2(As,P)2 polycrystalline bulk superconducting materials Open
This study is the first application of Bayesian optimization to the synthesis process of superconducting materials. As a model case, the phase purity of BaFe2(As,P)2 polycrystalline bulks, which affects their superconducting properties, wa…
View article: DMC-TPE: tree-structured Parzen estimator-based efficient data assimilation method for phase-field simulation of solid-state sintering
DMC-TPE: tree-structured Parzen estimator-based efficient data assimilation method for phase-field simulation of solid-state sintering Open
In phase-field simulations, accurate material parameters are required to quantitatively predict microstructural evolutions. Non-sequential data assimilations enable the estimation of unknown material parameters by minimizing a cost functio…
View article: Multi-Phase-Field Simulation of Non-Equilibrium Solidification in 316L Stainless Steel under Rapid Cooling Condition
Multi-Phase-Field Simulation of Non-Equilibrium Solidification in 316L Stainless Steel under Rapid Cooling Condition Open
Additive manufacturing has attracted much attention as a new technology for producing lightweight and high-strength materials. The multi-phase-field method has been used in powerful numerical simulations to predict solidification microstru…
View article: Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel
Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel Open
A modified SliceGAN architecture was proposed to generate a high-quality synthetic three-dimensional (3D) microstructure image of TYPE 316L material manufactured through additive methods. The quality of the resulting 3D image was evaluated…
View article: Phase-field modeling of solid-state sintering with interfacial anisotropy
Phase-field modeling of solid-state sintering with interfacial anisotropy Open
Sintered structures observed in experiments can consist of faceted crystal grains. To predict the formation of such structures, a new phase-field (PF) model of solid-state sintering that can analyze morphological changes and microstructura…
View article: Modulated Structure Formation in Dislocation Cells in 316L Stainless Steel Fabricated by Laser Powder Bed Fusion
Modulated Structure Formation in Dislocation Cells in 316L Stainless Steel Fabricated by Laser Powder Bed Fusion Open
Metal additive manufacturing enables producing complex geometric structures with high accuracy and breaks the design constraints of traditional manufacturing methods. Laser powder bed fusion, a typical additive manufacturing process, prese…
View article: Phase-field Modeling and Simulation of Solid-state Phase Transformations in Steels
Phase-field Modeling and Simulation of Solid-state Phase Transformations in Steels Open
The phase-field method is used as a powerful and versatile computational method to simulate the microstructural evolution taking place during solid-state phase transformations in iron and steel. This review presents the basic theory of the…
View article: Non-equilibrium multi-phase-field simulation for rapid solidification process in SUS316L stainless steel during additive manufacturing
Non-equilibrium multi-phase-field simulation for rapid solidification process in SUS316L stainless steel during additive manufacturing Open
A solidification during an additive manufacturing (AM) process is occurred under a highly non-equilibrium condition such as high cooling rate and large thermal gradient. In this study, we use the non-equilibrium multi-phase-field (NEMPF) m…
View article: Features of a Splashing Drop on a Solid Surface and the Temporal Evolution extracted through Image-Sequence Classification using an Interpretable Feedforward Neural Network
Features of a Splashing Drop on a Solid Surface and the Temporal Evolution extracted through Image-Sequence Classification using an Interpretable Feedforward Neural Network Open
This paper reports the features of a splashing drop on a solid surface and\nthe temporal evolution, which are extracted through image-sequence\nclassification using a highly interpretable feedforward neural network (FNN)\nwith zero hidden …
View article: BOXVIA: Bayesian optimization executable and visualizable application
BOXVIA: Bayesian optimization executable and visualizable application Open
Bayesian optimization (BO) has attracted attention in various research fields as a powerful probabilistic approach for solving optimization problems. To further facilitate the use of BO, we developed a graphical user interface-based Python…
View article: Numerial simulation of solid-state sintering of iron-based polycrystalline superconductors using first-principles calculation and phase-field method
Numerial simulation of solid-state sintering of iron-based polycrystalline superconductors using first-principles calculation and phase-field method Open
To improve performance of a high-temperature polycrystalline superconductor, it is effective to predict microstructural evolutions during a solid-state sintering using the phase-field method. However, interfacial and surface properties req…