Article
Materials Science, Multidisciplinary
T. W. J. Kwok, P. Gong, X. Xu, J. Nutter, W. M. Rainforth, D. Dye
Summary: The study found that cold rolling of the novel medium manganese steel led to an increase in strain hardening rate without a significant drop in ductility during subsequent tensile tests. Additional twinning systems activated by cold rolling provided potent nucleation sites for strain induced martensite, resulting in an enhanced TRIP effect.
METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE
(2022)
Article
Multidisciplinary Sciences
Jianing Xu, Yanju Wang, Jinyuan Yan, Bin Chen
Summary: The mechanical strengthening of metals, particularly in nanometer scale, remains a significant challenge and research focus in materials science. The use of rDAC XRD techniques has allowed for accurate tracking of strength changes in ultrafine metals, highlighting the size-dependent strengthening effect in nickel particles down to 3 nm.
JOVE-JOURNAL OF VISUALIZED EXPERIMENTS
(2021)
Article
Nanoscience & Nanotechnology
F. Khodabakhshi, A. P. Gerlich, D. Verma, M. Nosko, M. Haghshenas
Summary: This paper investigates the small-scale plasticity of UFG alloy and NS materials under different temperature conditions through nanoindentation testing and modeling results. It analyzes the influence of the interaction between dislocations and nanoparticles on material flow behavior, and discusses the impact of nanoparticles on grain boundary diffusion and sliding mechanisms.
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
(2021)
Article
Materials Science, Multidisciplinary
Y. X. Liu, Z. Y. Liang, S. G. Xu, M. X. Huang
Summary: This study investigates the size effect on mechanical properties and microstructural evolution of an ultra-thin 304L stainless steel sheet with less than 20 grains through the thickness. It is found that the surface grains exhibit weaker strain hardening compared to the interior grains due to enhanced annihilation of statistically stored dislocations. The finite element analysis shows good agreement with experimental results.
METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Xinguang Xiang, Yajie Zhang, Lu Jin, Zechao Li, Jinhui Tang
Summary: This paper proposes a novel fine-grained image hashing method, which achieves efficient fine-grained image retrieval by learning discriminative local features and constraining the generation of hash codes, containing diverse subtle local information.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Multidisciplinary Sciences
A. S. L. Subrahmanyam Pattamatta, David J. Srolovitz
Summary: In this study, a crystal thermodynamics framework is proposed to describe phase transformations induced by tensor stress in solids. The approach is based on nonlinear elasticity and first principles calculations. It allows for the prediction of phase transformations in grains of any orientation in different materials, regardless of the effects from the stress tensor.
NATURE COMMUNICATIONS
(2022)
Article
Materials Science, Multidisciplinary
Tao Wang, Tian Ye, Yong Feng, Kai-Xuan Wang, Yu-Xuan Du, Xiang-Hong Liu, Feng Zhao
Summary: Amorphization can be introduced in ultra-fine-grained copper through dynamic severe plastic deformation. High-resolution electron microscope observations reveal the transition from crystalline to amorphous state under extreme loading conditions, caused by profuse dislocations and stacking faults. Localized amorphization may be a potential mode of ultra-high strain rate deformation.
MRS COMMUNICATIONS
(2022)
Article
Engineering, Chemical
Junkai Fan, Jikang Li, Wei Liu, Chengpeng Wang
Summary: In this paper, a novel method called asymmetric gradient extrusion (AGE) for the preparation of ultra-fine-grained bulk materials is proposed. The deformation characteristics and grain splitting in AGE were analyzed using the slip line field method and compared with traditional extrusion. The experimental investigation confirmed the potential of AGE in the preparation of ultra-fine-grained bulk materials.
Article
Materials Science, Multidisciplinary
S. Wei, H. Zhang, C. Tangpatjaroen, J. Tarnsangpradit, A. D. Usta, M. Eriten, J. H. Perepezko, I. Szlufarska
Summary: The study found wear-induced grain refinement in UFG aluminum, with the mean contact stress playing a key role in the transition between grain growth and refinement. Hardness was observed to increase with decreasing grain size, deviating from the Hall-Petch relation. Differences in dislocation contents among samples prepared by different methods contributed to variations in wear resistance.
Article
Materials Science, Multidisciplinary
Hamdi Kuleyin, Recep Gumruk, Harun Yanar, Muhammet Demirtas, Gencaga Purcek
Summary: Notched stainless steel 304 L samples processed by ECAP at 500°C showed significantly improved mechanical properties, with nearly twice the collapse load and energy absorption values compared to traditional methods.
MECHANICS OF ADVANCED MATERIALS AND STRUCTURES
(2021)
Article
Computer Science, Artificial Intelligence
Zhicheng Sheng, Liqiang Nie, Meng Liu, Yinwei Wei, Zan Gao
Summary: In this paper, we propose FROND, a method for generating fine-grained talking videos. FROND addresses the challenges of capturing facial expressions, ensuring smooth transitions, and preserving details. Experimental results show that FROND outperforms several state-of-the-art baselines in both quantitative and qualitative evaluations.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Nanoscience & Nanotechnology
J. Zuo, T. Nakata, C. Xu, Y. P. Xia, H. L. Shi, X. J. Wang, G. Z. Tang, W. M. Gan, E. Maawad, G. H. Fan, S. Kamado, L. Geng
Summary: A high strength dilute Mg-0.8Al-0.1Ca-0.6Mn alloy wire was successfully developed by hot drawing, with the high strength attributed to the ultra-fine DRXed grains, coarse elongated unrecrystallized grains with dense dislocations, and nano sized Al2Ca and Al-Mn precipitates dispersed in the alloy wire.
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
(2022)
Article
Nanoscience & Nanotechnology
Qixiang Jia, Lei Chen, Zhibin Xing, Haoyu Wang, Miao Jin, Xiang Chen, Howook Choi, Heung Nam Han
Summary: This study presents a novel approach to design the microstructure of medium Mn steel by adjusting the austenite stability using hetero-structures. By coupling different effects, the steel achieved an ultra-high tensile strength and good elongation.
SCRIPTA MATERIALIA
(2022)
Article
Computer Science, Artificial Intelligence
Xiaohan Yu, Yang Zhao, Yongsheng Gao
Summary: This paper introduces SPARE, a self-supervised part erasing framework for ultra-fine-grained visual categorization. By performing random part erasing and prediction, the network learns discriminative representations and improves its learning capability.
PATTERN RECOGNITION
(2022)
Article
Nanoscience & Nanotechnology
S. H. Sun, M. H. Cai, H. Ding, H. L. Yan, Y. Z. Tian, S. Tang, Peter Hodgson
Summary: This study reports a tri-phase hierarchical lamellar structure to achieve a balance between ultra-high yield strength and high ductility in a Si-Al added medium Mn lightweight TRIP/TWIP steel. By subjecting the warm-rolled sample to small cold rolling and low-temperature tempering, a nano-scale twins and martensitic laths were obtained in the austenitic matrix along with the hard delta-ferrite and nano-precipitates. The tempered sample exhibited a yield strength of 1403 MPa, which was 370 MPa higher than the warm-rolled counterpart. The propagation of plastic strain was confined by the hard zone (delta-ferrite), resulting in an intrinsic hetero-deformation induced (HDI) strengthening effect. The large ductility of 30% was attributed to the elongation of the yield point and enhanced strain hardening after the Luders strain, which were closely associated with HDI hardening and additional TRIP/TWIP effects.
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
(2022)
Article
Materials Science, Multidisciplinary
Daeyoung Kim, Hansol Maeng, Young Choi, Hyunjoo Choi, Seok-Jae Lee
Summary: The study proposed a constitutive model to predict the tensile behavior of Al-Mg-Si alloy based on the characteristics of aging precipitates, and demonstrated the impact of various precipitates on strength through different aging treatments.
METALS AND MATERIALS INTERNATIONAL
(2021)
Article
Materials Science, Multidisciplinary
Seunggyu Choi, Junhyub Jeon, Namhyuk Seo, Seung Bae Son, Seok-Jae Lee
Summary: This study investigated the effects of heating rate during solid solution heat treatment on the mechanical properties and microstructure of 7055 aluminum alloy. Dilatometric tests were conducted to control the heating rates, and a variety of methods were used to evaluate mechanical properties and microstructural features. Characteristics of precipitates for each heating rate were calculated through thermodynamic simulation, and a model for predicting mechanical properties was proposed based on the results.
METALS AND MATERIALS INTERNATIONAL
(2021)
Article
Materials Science, Multidisciplinary
Junhyub Jeon, Gwanghun Kim, Namhyuk Seo, Hyunjoo Choi, Hwi-Jun Kim, Min-Ha Lee, Hyun-Kyu Lim, Seung Bae Son, Seok-Jae Lee
Summary: Ni-based amorphous alloys have unique physical properties and are attracting attention in biomass plants. Machine learning algorithms are used to design and predict the thermal properties of these alloys, with a focus on determining the optimal composition.
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
(2022)
Article
Nanoscience & Nanotechnology
Hyun Wook Lee, Tak Min Park, Namhyuk Seo, Seok-Jae Lee, Changmin Lee, Jeongho Han
Summary: This study aimed to develop cost-effective steels for cryogenic applications by investigating the microstructural evolutions and impact absorbed energy of a newly designed Fe-2Mn-5Ni-0.1C steel treated with quenching-tempering (QT) and quenching-lamellarizing-tempering (QLT) processes. The QLT-processed steel exhibited a higher impact absorbed energy than the QT-processed steel and Fe-9Ni steel at -196 degrees C, thanks to the active transformation-induced plasticity from retained austenite and pronounced plastic deformation of the soft martensitic matrix due to double-step tempering.
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
(2022)
Article
Materials Science, Multidisciplinary
Junhyub Jeon, Namhyuk Seo, Seung Bae Son, Jae-Gil Jung, Seok-Jae Lee
Summary: This study accurately predicts the carbon diffusivity in steels using machine learning methods and provides specific insights into the prediction mechanisms of features.
JOURNAL OF MATERIALS SCIENCE
(2022)
Article
Materials Science, Multidisciplinary
Junhyub Jeon, DongEung Kim, Jun-Ho Hong, Hwi-Jun Kim, Seok-Jae Lee
Summary: We investigated various numerical methods to predict the hardness of tempered martensite in low alloy steels, including physical-based empirical equation, linear regression, shallow neural network, and deep learning approaches. We found that the physical-based empirical equation and the regression model based on the response surface method had similar prediction accuracy. The prediction accuracy of the machine learning models improved with increased complexity, but overfitting became a concern. Interestingly, a single layered neural network model with optimized hyperparameters showed similar or better hardness prediction performance compared to deep learning models with more complex architectures.
KOREAN JOURNAL OF METALS AND MATERIALS
(2022)
Article
Materials Science, Multidisciplinary
Junhyub Jeon, Namhyuk Seo, Jae -Gil Jung, Seung Bae Son, Seok-Jae Lee
Summary: This paper presents a machine learning model for predicting the Acm temperature in the Fe-C phase diagram. The dataset is analyzed and adjusted, and the model is verified and analyzed using various techniques such as cross-validation and Shapley additive explanations.
MATERIALS TRANSACTIONS
(2022)
Article
Materials Science, Multidisciplinary
Jungjoon Kim, Dongchan Min, Suwon Park, Junhyub Jeon, Seok-Jae Lee, Youngkyun Kim, Hwi-Jun Kim, Youngjin Kim, Hyunjoo Choi
Summary: Densification of amorphous powder is crucial for energy-conversion parts. Mixing powders of different sizes enhances densification. Analytical model and computational simulation were used to predict powder packing behavior, and a machine learning model achieved high packing fraction.
MATERIALS TRANSACTIONS
(2022)
Article
Materials Science, Multidisciplinary
Junhyub Jeon, Namhyuk Seo, Jae-Gil Jung, Hee-Soo Kim, Seung Bae Son, Seok-Jae Lee
Summary: In this study, a machine-learning model is used to predict austenite-grain growth, and explainable artificial intelligence (XAI) is applied to analyze the variable importance and mechanisms. With a large amount of collected data and the elimination of outliers using statistical methods, random forest regression (RFR) is selected as the model. The results show an improvement in the accuracy of the machine-learning model.
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
(2022)
Article
Nanoscience & Nanotechnology
Jungbin Park, Junhyub Jeon, Namhyuk Seo, Singon Kang, Seung Bae Son, Seok-Jae Lee, Jae-Gil Jung
Summary: The evolution of microstructure and mechanical properties of AISI 4340 steel during high-energy ball milling, spark plasma sintering (SPS), and post heat treatments was investigated. The study found that high-energy ball milling resulted in the formation of a nanocrystalline (-10 nm) bcc Fe matrix with segregation of alloying elements and oxide particles. The as-sintered alloy consisted of martensite-austenite (MA) constituent and fine pearlite, while the quenching after austenitization formed a microstructure composed of martensite and MA constituent. Tempering induced the decomposition of retained austenite and precipitation of cementite particles. The compressive yield strength of the as-sintered alloy was primarily strengthened by dislocations and grain boundaries/cementite lamellae, as well as secondary strengthening by oxide particles.
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
(2023)
Article
Metallurgy & Metallurgical Engineering
Gwanghun Kim, Jungbin Park, Seok-jae Lee, Hee-soo Kim
Summary: Cu-Sn alloys, known as bronze, have been widely used for various purposes since ancient times. This study focuses on the Cu-22Sn alloy with a higher tin content than traditional bronze, which is difficult to manufacture by conventional casting methods due to the carbon solubility of copper and tin. Cu-22Sn-xC alloy was successfully fabricated using mechanical alloying and spark plasma sintering, and its microstructural characteristics were analyzed. The hardness of sintered Cu-22Sn-xC alloy was compared with Cu-22Sn alloys manufactured by rolling, casting, and forging, and B0 sintered alloy showed the highest hardness.
ARCHIVES OF METALLURGY AND MATERIALS
(2023)
Article
Metallurgy & Metallurgical Engineering
Jungbin Park, Jonghyun Jeon, Namhyuk Seo, Gwanghun Kim, Seung Bae Son, Jae-Gil Jung, Seok-Jae Lee
Summary: The stability of austenite and the strain-induced martensitic transformation behavior of a nanocrystalline FeNiCrMoC alloy were studied. The alloy was prepared by high-energy ball milling and spark plasma sintering. X-ray diffraction was used to measure the phase fraction and grain size. The grain sizes of the milled powder and sintered alloy were found to be in the nanometer range. The variation in austenite fraction during compressive deformation was measured, and the austenite stability and strain-induced martensitic transformation behavior were calculated. Hardness measurements were performed to assess the mechanical properties, and the hardness increased to 64.03 HRC when compressed up to 30%.
ARCHIVES OF METALLURGY AND MATERIALS
(2023)
Article
Metallurgy & Metallurgical Engineering
Seong-Min So, Ki-Yeon Kim, Il -Song Park, Seok-Jae Lee, Dong-Jin Yoo, Yeon-Won Kim, Min -Suk Oh
Summary: A Si-Fe-Al ternary oxide-based micropowder coating was applied to prevent the formation of Zn coating on steel during hot-dip galvanizing process, reducing welding fume and defects in Zn-galvanized steel welding. The optimized oxide coating remained stable at 470 degrees C and effectively inhibited Zn coating formation. Residual Zn could be easily removed mechanically. This coating reduced Zn fume and prevented Zn from melting in weld bead during high-temperature welding, thereby reducing welding defects. The study showed that this pretreatment simplifies manufacturing process and saves time cost-effectively.
ARCHIVES OF METALLURGY AND MATERIALS
(2023)
Article
Metallurgy & Metallurgical Engineering
Min Woo Lee, Young Sin Choi, Do Hun Kwon, Eun Ji Cha, Hee Bok Kang, Jae In Jeong, Seok Jae Lee, Hwi Jun Kim
Summary: In this study, artificial intelligence and machine learning were used to optimize the amount of metalloid elements added to a Fe-based amorphous alloy to enhance its soft magnetic properties. The effects of metalloid elements on magnetic properties, such as saturation magnetization and coercivity, were investigated through correlation analysis. Regression analysis using the Random Forest Algorithm was performed, and the coefficient of determination was found to be 0.95. Furthermore, when considering the phase information of the Fe-Si-B-P ribbon, the coefficient of determination increased to 0.98. The optimal range of metalloid addition was predicted using correlation analysis and machine learning.
ARCHIVES OF METALLURGY AND MATERIALS
(2022)
Article
Metallurgy & Metallurgical Engineering
Young-sin Choi, Do-hun Kwon, Min-woo Lee, Eun-ji Cha, Junhyup Jeon, Seok-jae Lee, Jongryoul Kim, Hwi-jun Kim
Summary: The soft magnetic properties of Fe-based amorphous alloys can be controlled through alloy design, but there is a discrepancy between experimental data and predicted values. Machine learning processes can be used to optimize the composition for further improvement of the soft magnetic properties.
ARCHIVES OF METALLURGY AND MATERIALS
(2022)
