Keio University · Engineering
Professor Mayu Muramatsu's research lab specializes in multiscale materials modeling and machine learning-driven materials design, focusing on polymer composites, nanocrystalline metals, and carbon fiber-reinforced plastics (CFRP). The lab develops advanced computational frameworks that integrate machine learning—particularly generative models and convolutional neural networks—with multiscale simulation techniques to enable forward prediction and inverse design of microstructures from desired mechanical properties. A key focus is on nondestructive evaluation and defect characterization in advanced composites using infrared thermography and inverse analysis, aiming to enhance structural integrity and performance in engineering applications.
Figures are computed from collected data and may differ slightly.
In this study, using some machine learning methods, we develop a framework that deals with forward analysis to predict a property from a polymer alloy’s phase separation structure and inverse design to generate the structure from the property. We only consider Young’s modulus as the property in this study. The forward analysis is performed using a convolutional neural network (CNN) and the inverse design is realized by a random search toward a model combining a generative adversarial network (GA
Abstract This study aims to develop a multiscale bridging method for investigating nanocrystalline metals based on macro-scale deformation. For this purpose, we propose a hierarchical multiscale computational method that can focus on some of the elements in a finite element model for scale bridging to atomistic-scale models. This method assumes that atomistic-scale nanocrystalline models are related to the integration points in a finite element and deform based on the macro-scale deformation. Na
AbstractIn this study, inverse analyses of the defects in carbon fiber-reinforced plastics (CFRPs) are performed using the transfer matrix approach. The material properties used in the calculation were obtained on the basis of mixture laws for epoxy resin and carbon fibers. The accuracy of the inverse analysis was confirmed by calculations employing numerical models of CFRP plates with PAN-based and pitch-based carbon fibers containing defects. The inverse analysis was conducted based on the tem
Carbon-fiber-reinforced plastic (CFRP) is a composite material whose base material is plastic and reinforcement material is carbon fibers. CFRP is widely used in various fields for laminating prepregs. The laminated plate tends to sustain damage, such as delamination, fiber breakage, and base material breakage; hence, we must conduct high-precision and efficient nondestructive testing (NDT). Examples of NDT are ultrasonic examination, X-ray tomography, and infrared stress analysis. With most NDT
Purpose The purpose of this study is to develop a simulation method to calculate non-stationary distributions of the chemical potential of oxygen in a solid oxide fuel cell (SOFC) under operation. Design/methodology/approach The initial-boundary value problem was appropriately formulated and the appropriate boundary conditions were implemented so that the problem of non-stationary behavior of SOFC can be solved in accordance with actual operational and typical experimental conditions. The depend
In this study, the correlation between the stress–strain behavior of a carbon fiber-reinforced plastic (CFRP) and the temperature at which the heat-affected zone (HAZ) is generated is investigated. First, CFRP ([−45/45]2s laminate) specimens were heated at several temperatures to induce thermal damage, i.e. a HAZ. Subsequently, tensile tests were conducted on the specimens with thermal damage. Then, microscopy and X-ray measurements were carried out to discuss the stress–strain responses from a
We calculate the parameter that governs the width of the transition zone by molecular dynamics (MD) simulation and use it in a phase-field crack (PFC) simulation with the mechanical properties of iron. First, a quantitative evaluation of intactness is conducted by examining the change in atomic conformation induced by crack propagation, whose numerical data are taken from the result of the MD simulation. The spatial distribution of the intactness is fitted to the same function as the damage para
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