著者

柳川 真之裕 渡邊 育夢

出版者

一般社団法人 日本鉄鋼協会

雑誌

鉄と鋼 (ISSN:00211575)

巻号頁・発行日

pp.TETSU-2023-080, (Released:2023-10-19)

参考文献数

21

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Yield-point phenomena in Ferrite–Pearlite duplex steels were investigated using multi-scale computational simulations. In this multi-scale simulations, stress–strain relationship of Ferrite phase was characterized by an elastoplastic constitutive model considering yield-drop behavior and its material constants were determined by minimizing residual error between a computational simulation and experiment of tensile test, where yield-point phenomenon in a tensile test of Ferrite steel was reproduced.Using the determined material response of Ferrite phase, finite element analyses of Ferrite–Pearlite duplex microstructure were performed to examine its macroscopic material response and its microscopic deformation mechanism. Besides, finite element analyses of tensile test based on the numerical results of microscopic finite element analysis were conducted to reproduce yield-point phenomena in Ferrite–Pearlite duplex steels.

著者

アギアル デ ソウザ ヴィニシウス 渡邊 育夢 柳田 明

出版者

一般社団法人 日本機械学会

雑誌

M&M材料力学カンファレンス

巻号頁・発行日

vol.2013, pp._OS0805-1_-_OS0805-3_, 2013

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The purpose of this study is to improve and extend a method to estimate the friction coefficient in equal channel angular extrusion (ECAE) including cases with back pressure. Employing three dimensional finite element analysis (FEA) and Coulomb friction model with truncated shear stress, the relationship between the friction coefficient and the reaction force is investigated in ECAE with back pressure. Additionally, the deformation state of the billet after ECAE and the pressing load are compared between the experiment and the simulation results with the same friction coefficient for validation of the numerical model.

著者

安藤 玲音 松野 崇 松田 知子 山下 典理男 横田 秀夫 後藤 健太 渡邊 育夢

出版者

一般社団法人 日本鉄鋼協会

雑誌

鉄と鋼 (ISSN:00211575)

巻号頁・発行日

vol.106, no.12, pp.944-952, 2020 (Released:2020-11-30)

参考文献数

21

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Herein, we investigated the local preliminary hardening of ferrite near the ferrite–martensite interfaces in a dual-phase (DP) steel. Geometrically necessary dislocations (GNDs), generated due to interfacial misfit between different phases, may cause preliminary hardening of ferrite around such interfaces. However, for nano-hardness distribution, the hardened zone was not evidently detected by scattering measurement. Thus, we factorized nano-hardness scattering to estimate the actual ferrite hardness near ferrite–martensite interfaces.First, nano-hardness was measured around a martensite island using a conical nano-indenter in the DP steel containing 10% martensite by volume. Taking into account the scattering, the nano-hardness measurement converged to the hardness of ferrite, exceeding the distance corresponding to the nano-indenter radius. Thus, a preliminary hardening zone was not detected. Subsequently, the surface of the nano-indented microstructure was polished and observed using scanning electron microscopy (SEM) by analyzing electron back scattering diffraction (EBSD). This analysis confirmed the presence of the nano-indented microstructure under ferrite. Moreover, it established that the majority of the irregularly higher nano-hardness was caused by the buried martensite under ferrite. The value of the kernel average misorientation (KAM), which is proportional to the GND density for other irregularly higher nano-hardness points, was higher for the nano-indented microstructure as compared to that of the buried martensite. On the other hand, the ferrite was expanded under the nano-indented points for the majority of the irregularly lower nano-hardness, with some exceptions. Further, soft martensite was observed to induce irregularly lower nano-hardness locally around the interface.

著者

肥沼 康太 山中 晃徳 渡邊 育夢 桑原 利彦

出版者

一般社団法人 日本塑性加工学会

雑誌

塑性と加工 (ISSN:00381586)

巻号頁・発行日

vol.61, no.709, pp.48-55, 2020 (Released:2020-02-25)

参考文献数

31

被引用文献数

1

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Deformation of an aluminum alloy sheet is affected by its underlying crystallographic texture and has been widely studied by the crystal plasticity finite element method (CPFEM). The numerical material test based on the CPFEM allows us to quantitatively estimate the stress-strain curve and the Lankford value (r-value), which depend on the texture of aluminum alloy sheets. However, in the use of the numerical material test as a means of optimizing the texture to design aluminum alloys, the CPFEM is computationally expensive. We propose a methodology for rapidly estimating the stress -strain curve and r-value of aluminum alloy sheets using deep learning with a neural network. We train the neural network with synthetic texture and stress-strain curves calculated by the numerical material test. To capture the features of synthetic texture from a {111} pole figure image, the neural network incorporates a convolution neural network. Using the trained neural network, we can estimate the uniaxial stress-strain curve and the in-plane anisotropy of the r-value for various textures that contain Cube and S components. The results indicate that the neural network trained with the results of the numerical material test is a promising methodology for rapidly estimating the deformation of aluminum alloy sheets.