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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

Abstract

This paper reviews various inverse analysis models used in steel material design, with a focus on integrating process, microstructure, and properties through advanced machine learning techniques. The study underscores the importance of establishing comprehensive models that effectively link these elements for enhanced materials engineering. Key models discussed include the convolutional neural network–artificial neural network‐coupled model, which employs convolutional neural networks for feature extraction; the Bayesian‐optimized generative adversarial network–conditional generative adversarial network model, which generates diverse virtual microstructures; the multi‐objective optimization model, which concentrates on process–property relationships; and the microstructure–process parallelization model, which correlates microstructural features with process conditions. Each model is assessed for its strengths and limitations, influencing its practical applicability in material design. The paper concludes by advocating for continued improvements in model accuracy and versatility, with the ultimate goal of enhancing steel properties and expanding the scope of data‐driven material development.

DOI： 10.1002/mgea.71

Open Access

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IOP Publishing  

Abstract

A cellular automaton simulation of grain growth in a solid phase was conducted, considering the temperature gradient, heat source movement, and conditions favoring prioritized grain growth. The results reveal that, under optimal conditions, cellular grains elongated in the direction of the heat source movement. Detailed simulations illustrate the dynamics of grain growth and effect of mobility and driving forces on the dynamics, providing valuable insights into cellular grain growth in a solid phase.

DOI： 10.1088/1361-651x/ad7bda

Open Access

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Other Link： https://iopscience.iop.org/article/10.1088/1361-651X/ad7bda/pdf

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.mtcomm.2024.110360

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Informa UK Limited  

DOI： 10.1080/14686996.2024.2388501

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Language：English   Publishing type：Research paper (scientific journal)  

<jats:p>We performed a machine learning-aided analysis of the rolling and recrystallization textures in pure iron with different cold reduction ratios and cold-rolling directions. Five types of specimens with different cold reduction ratios and cold-rolling directions were prepared. The effect of two-way cold-rolling on the rolling texture was small at cold reduction ratios different from 60%. The cold reduction ratio in each stage hardly affected the texture evolution during cold-rolling and subsequent short-term annealing. In the case of long-term annealing, although abnormal grain growth occurred, the crystal orientation of the grains varied. Moreover, the direction of cold-rolling in each stage also hardly affected the texture evolution during cold-rolling and subsequent short-term annealing. During long-term annealing, sheets with the same cold-rolling direction in the as-received state and in the first stage showed the texture evolution of conventional one-way cold-rolled pure iron. Additionally, we conducted a machine learning-aided analysis of rolling and recrystallization textures. Using cold-rolling and annealing conditions as the input data and the degree of Goss orientation development as the output data, we constructed high-accuracy regression models using artificial neural networks and XGBoost. We also revealed that the annealing temperature is the dominant factor in the nucleation of Goss grains.</jats:p>

DOI： 10.3390/ma17143402

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1016/j.commatsci.2024.113143

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.rinma.2023.100526

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1080/27660400.2024.2320691

Open Access

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Language：English   Publishing type：Research paper (scientific journal)  

In this study, the tensile properties of tempered martensite steel were analyzed using a combination of an experimental approach and deep learning. The martensite steels were tempered in two stages, and fine and coarse cementite particles were mixed through two-stage tempering. The samples were heated to 923 and 973 K and held isothermally for 30, 45, and 60 min. They were then cooled to 723, 773, and 823 K; held isothermally for 30, 45, and 60 min; and furnace-cooled to room temperature (296 ± 2 K). The combination of low tempering temperature and short holding time in the first stage resulted in high tensile strength. When the tempering temperature at the first stage was 923 K, the combination of low tempering temperature and long holding time at the second stage resulted in high total elongation. This means that decreasing the number of coarse cementite particles and increasing the number of fine cementite particles improve the strength–ductility balance. Using the results obtained by the experimental approach, an image-based regression model was constructed that can accurately suggest the relationship between the microstructure and tensile properties of tempered martensite steel. We succeeded in developing image-based regression models with high accuracy using a convolutional neural network (CNN). Moreover, gradient-weighted class activation mapping (Grad-CAM) suggested that fine cementite particles and coarse and spheroidal cementite particles are the dominant factors for tensile strength and total elongation, respectively.

DOI： 10.3934/MATERSCI.2024050

Open Access

Scopus

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1080/14686996.2022.2157682

Open Access

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI AG  

The abnormal grain growth of steel, which is occurs during carburization, adversely affects properties such as heat treatment deformation and fatigue strength. This study aimed to control abnormal grain growth by controlling the materials and processes. Thus, it was necessary to investigate the effects of microstructure, precipitation, and heat treatment conditions on abnormal grain growth. We simulated abnormal grain growth using the cellular automaton (CA) method. The simulations focused on the grain boundary anisotropy and dispersion of precipitates. We considered the effect of grain boundary misorientation on boundary energy and mobility. The dispersion state of the precipitates and its pinning effect were considered, and grain growth simulations were performed. The results showed that the CA simulation reproduced abnormal grain growth by emphasizing the grain boundary mobility and the influence of the dispersion state of the precipitate on the occurrence of abnormal grain growth. The study findings show that the CA method is a potential technique for the prediction of abnormal grain growth.

DOI： 10.3390/ma17010138

Open Access

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/ma16247536

Open Access

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.matdes.2023.112548

Open Access

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/ma16216922

Open Access

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

DOI： 10.1002/adma.202302530

PubMed

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1080/27660400.2022.2129508

Open Access

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Language：English   Publishing type：Research paper (scientific journal)  

Mechanical metamaterials are artificial structures with structure-dependent properties. They often harness zero-energy deformation modes, e.g., a single shape change that limits their applications, resulting in the need for changeable mechanical responses. We address this limitation by using a flexible material, called light-responsive shape-memory polydimethylsiloxane (SM-PDMS), to introduce reprogrammability into flexible mechanical metamaterials. The SM-PDMS is a rubber-like functional material with shape-memory and photothermal effects. Specfically, we propose three different reprogrammable SM-PDMS metamaterials with different mechanical responses, namely, an auxetic SM-PDMS, a chiral SM-PDMS, and a buckling-induced SM-PDMS. Finally, a buckling-induced SM-PDMS was harnessed to make a soft actuator with a reprogrammable preferred locomotion direction. Despite focusing on reprogramming flexible metamaterials using the light-induced SM effect, our strategy can be easily extended to other structures and smart materials. More importantly, our strategy paves the way to change the mechanical responses for similar architectures. Furthermore, our designed flexible metamaterials have the potential for different applications, such as soft robots, actuation, adaptive safety, and sports equipment.

DOI： 10.1016/j.apmt.2022.101662

Open Access

Scopus

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER SCIENCE INC  

Wire and arc additive manufacturing (WAAM) is suitable for fabricating multimaterial dense structures in various additive manufacturing technologies. Watanabe et al. [1] reported that a unique two-stage stress-strain curve was observed during tensile testing of a duplex multilayer steel structure fabricated by WAAM. The material behavior was explained by the transformation-induced plasticity, which was attributed to a decrease in the austenite phase observed after testing using X-ray diffraction at three representative points of the tensile test specimen. However, the deformation mechanism was unclear from the viewpoint of the composite structure mechanics. In this study, the heterogeneous microstructure of the steel structure was investigated for the duplex layer region to infer the relationship between the material response and underlying deformation mechanism. The distributions of the phase and major alloy elements indicated that the layers melted and mixed during WAAM, and the multilayer structure subsequently changed compared to the design layout. The WAAM structure was composed of two dual-phase layers containing different volume fractions of the martensite phase. Hence, the austenite phase in the martensite-rich layer initially deformed and then transformed to the martensite phase during tensile testing. Consequently, the strength of the martensite-rich layer was recovered to the micro-mechanically estimated level and the two-stage stress-strain curve was generated. Thus, this paper presents the potential of multimaterial WAAM for controlling microscopic heterogeneities and their material responses by adjustment of the process parameters.

DOI： 10.1016/j.matchar.2022.112159

Web of Science

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：WILEY-V C H VERLAG GMBH  

Shape-reconfigurable materials are crucial in many engineering applications. However, because of their isotropic deformability, they often require complex molding equipment for shaping. A polymeric origami structure that follows predetermined deformed and non-deformed patterns at specific temperatures without molding is demonstrated. It is constructed with a heterogeneous (dynamic and static) network topology via light-induced programming. The corresponding spatio-selective thermal plasticity creates varied deformability within a single polymer. The kinematics of site-specific deformation allows guided origami deployment in response to external forces. Moreover, the self-locking origami can fix its geometry in specific states without pressurization. These features enable the development of shape-reconfigurable structures that undergo on-demand geometry changes without requiring bulky or heavy equipment. The concept enriches polymer origamis, and could be applied with other polymers having similar chemistries. Overall, it is a versatile material for artificial muscles, origami robotics, and non-volatile mechanical memory devices.

DOI： 10.1002/smll.202107078

Web of Science

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Informa {UK} Limited  

DOI： 10.1080/14686996.2021.2025426

Open Access

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier {BV}  

DOI： 10.1016/j.matdes.2021.110178

Open Access

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.3390/cryst11111316

Open Access

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Informa {UK} Limited  

DOI： 10.1080/27660400.2021.1959838

Open Access

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