Carbon neutrality of the steel industry requires the development of high-strength steel. The mechanical properties of low-alloy steel can be considerably improved at a low cost by adding a small ...amount of titanium (Ti) element, namely Ti microalloying, whose performance is related to Ti-contained second phase particles including inclusions and precipitates. By proper controlling the precipitation behaviors of these particles during different stages of steel manufacture, fine-grained microstructure and strong precipitation strengthening effects can be obtained in low-alloy steel. Thus, Ti microalloying can be widely applied to produce high strength steel, which can replace low strength steels heavily used in various areas currently. This article reviews the characteristics of the chemical and physical metallurgies of Ti microalloying and the effects of Ti microalloying on the phase formation, microstructural evolution, precipitation behavior of low-carbon steel during the steel making process, especially the thin slab casting and continuous rolling process and the mechanical properties of final steel products. Future development of Ti microalloying is also proposed to further promote the application of Ti microalloying technology in steel to meet the requirement of low-carbon economy.
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•Fe-6.5 %Si ultra-thin ribbons with a thickness of 35 μm are prepared by planar flow casting.•Desired microstructure and magnetic property are obtained by low strain cold-rolling and ...annealing.•Plastic deformation reduces the activation energy for grain growth in the ribbon.
Planar flow casting (PFC) has been applied to prepare Fe-6.5wt. %Si ultra-thin ribbons with a strong 〈001〉 fiber texture. However, critical issues such as fine grain size and large surface roughness are vital to overcome. In this study, low strain cold-rolling and subsequent annealing are applied to planar flow casting Fe-6.5 %Si ribbons. Cold-rolling reduces surface roughness and introduces heterogeneous deformation in the ribbons. Recrystallization occurs preferentially in high deformed areas during annealing. Recrystallizing grains grow significantly by consuming neighboring deformed grains with fine size and 〈001〉 orientation, leading to greatly reduced iron loss. Thus, the ribbon annealed at 1000 ℃ exhibits the best combination of magnetic properties with B8 of 1.29 T and P2/5000 of 6.95 W/kg. The kinetics of recrystallization and grain growth are described along with the calculations of activation energies, which suggests the cold-rolled ribbon has a faster growth rate than the undeformed one. Two mechanisms, namely strain-induced boundary migration (SIBM) and thermo-induced boundary migration (TIBM), are used to explain the grain growth. SIBM is dominant at low temperatures while TIBM is dominant at high temperatures. This work presents a strategy to tailor the microstructures and properties of PFC-prepared alloys with fine grain.
Internet economy, with its diversified forms and efficient and convenient operation mode, has a significant impact on Residents' consumption behavior, consumption structure and financial investment. ...We should actively improve the residents' awareness of rational consumption, optimize the consumption structure, strengthen the regulation and guidance of Internet finance, build a diversified financial investment product system, improve the social security system, and enhance the residents' confidence in Internet financial investment, so as to realize the overall progress and development of society in the era of Internet economy.
Dielectric composites boost the family of energy storage and conversion materials as they can take full advantage of both the matrix and filler. This review aims at summarizing the recent progress in ...developing high‐performance polymer‐ and ceramic‐based dielectric composites, and emphases are placed on capacitive energy storage and harvesting, solid‐state cooling, temperature stability, electromechanical energy interconversion, and high‐power applications. Emerging fabrication techniques of dielectric composites such as 3D printing, electrospinning, and cold sintering are addressed, following by highlighted challenges and future research opportunities. The advantages and limitations of the typical theoretical calculation methods, such as finite‐element, phase‐field model, and machine learning methods, for designing high‐performance dielectric composites are discussed. This review is concluded by providing a brief perspective on the future development of composite dielectrics toward energy and electronic devices.
Recent progresses in polymer‐based and ceramic‐based dielectric composite materials for energy storage and conversion are selectively reviewed with an attention to capacitive energy storage, energy harvesting, solid‐state cooling, electromechanical energy interconversion, and high power applications. Emerging fabrication techniques such as 3D printing, electrospinning, cold sintering, and typical theoretical calculation frameworks, such as finite‐element, phase‐field model, and machine learning for designing high‐performance dielectric composites are discussed.
The effect of quenching and tempering (QT) process on the mechanical properties of the experimental high-strength low-alloy (HSLA) steel was analyzed by Grey-Taguchi method, thus achieving the ...optimum combination of parameters based on the appropriate nine sets of experiments on an orthogonal array. The grey relational analysis (GRA) reveals that the quenching temperature T1 has the greatest effect on each response variable, followed by the tempering temperature T2, while the quenching time t1 and tempering time t2 have similar and the least effect. Experimental assessment of microstructure evolution was performed by multi-scale characterizations and in-situ investigation combined with modeling, focusing on martensite transfomation kinetics that controlled by the quenching process. Results indicate that the prior austenite grain (PAG) size and the substructure of martensite are significantly refined, as the quenching temperature decreased from 950 to 850 °C. The PAG refinement leads to an increase in driving force for martensite transformation initiation, thus shifting the martensite transformation temperature to lower levels for a higher degree of undercooling, consistent with the experimental and modeling results. The refined microstructure obtained at low quenching temperature contributes to strength improvement, and the various carbides precipitated during tempering process offset the tempering softening of the steels. In general, the value increase of process parameters (T1, T2, t1 and t2) leads to a decrease of strength property, but an increase of ductility and toughness. Based on the theoretical and experimental basis, a 1 GPa grade HSLA steel with improved comprehensive mechanical properties can be produced.
The effect of microstructure on crack resistance and cryogenic toughening in a 3.5 wt% Ni high-strength low-alloy (HSLA) steel was investigated. Multistage heat treatments involving quenching (Q), ...lamellarization (L), and tempering (T) were applied to prepare the HSLA steels with various microstructures, focusing on the reverse transformation and reconfiguration of martensite, as well as its influence on impact crack formation and propagation behavior by multi-scale characterizations. The results indicate that lamellarization treatment has little influence on tensile properties, but significantly improves impact toughness in the QL and QLT specimens, which exhibit over 30 % increment in Charpy V-notch (CVN) absorbed energy Et tested in range from RT to −196 °C, over 25 °C decrement in ductile-brittle transition temperature (DBTT), and much higher crack propagation energy Ep and higher ratio of Ep/Et, as compared with the as-quenched (AQ) and QT specimens. The lamellarization treatment also contributes to a significant refinement effect on martensitic block size, caused by fresh martensite transformation from the reversed austenite, resulting in an increment in high angle grain boundaries (HAGBs), with introduction of a small amount of retained austenite (RA) as well. Therefore, the impact crack resistance and cryogenic toughness is improved in specimens processed with lamellarization treatment, due to the enhancement in crack deflection and hindering effect of the HAGBs, as well as toughening effect by in situ austenite-to-martensite transformation of the RA. Based on the present study, a 1 GPa grade HSLA steel with high ductility and excellent cryogenic toughness can be produced.
Strain-induced precipitation in a Ti micro-alloyed HSLA steel Wang, Zhenqiang; Mao, Xinping; Yang, Zhigang ...
Materials science & engineering. A, Structural materials : properties, microstructure and processing,
11/2011, Letnik:
529
Journal Article
Recenzirano
► Strain induces TiC precipitation in austenite. ► TiC precipitation occurs on dislocations and dislocation sub-structures. ► Severe deformation brings out more dislocations in steel. ► Higher ...dislocation density results in faster kinetics of precipitation.
The strain-induced precipitation kinetics of TiC in a 0.05% C–0.10% Ti HSLA steel was investigated by two-stage interrupted compression method. The precipitation–time–temperature (PTT) diagram for TiC precipitation was obtained by analyzing the softening kinetics curves of deformed austenite, which was confirmed to be of validity by employing transmission electron microscopy (TEM). Experimental results showed that the PTT diagram for TiC precipitation exhibited a typical “C” shaped and the nucleation of strain-induced TiC precipitation was a very rapid process in the temperature range 900–925
°C. The relatively severe deformation applied on the steel was considered to be the main factor resulting in the fast kinetics of TiC precipitation. The TiC precipitates were heterogeneously distributed in either a chain-like or a cell like manner, implying that the precipitates nucleated on dislocations or on dislocation sub-structures, which were produced by deformation. The growth of TiC precipitates approximately followed a parabolic law. In addition, the coarsening of strain-induced TiC precipitates had already started before the completion of precipitation.
Tantalum and its alloys are regarded as equipment construction materials for processing aggressive acidic media due to their excellent properties. In this study, the influence of severe rolling (90%) ...on the dissolution rate of a cold-rolled Ta-4%W sheet in different directions was investigated during immersion testing and the corresponding mechanism was discussed. The results show that the dissolution rate of the cold-rolled sample is significantly lower than that of the undeformed sample. The corrosion resistance followed the sequence of “initial” < “90%-ND” < “90%-RD” < “90%-TD”, while the strength is in positive correlation with the corrosion resistance. Severe rolling promotes grain subdivision accompanied by long geometrically necessary boundaries and short incidental dislocation boundaries on two scales in the cold-rolled sample. The volume elements enclosed by geometrically necessary boundaries form preferential crystallographic orientations. Such preferential crystallographic orientations can greatly weaken the electrochemical process caused by adjacent volume elements, resulting in greatly reduced corrosion rates in the severely deformed sample. The unexpected finding provides a new idea for tailoring the structures of tantalum alloys to improve both their strength and corrosion resistance.
•The hybrid machine learning model is established to predict SH-CCT diagrams of steels.•The predicted values of models have a high consistency with the experimental values.•The mathematical ...expression of hardness is given accurately by symbolic regression.•It can guide the welding process with a desired microstructure and properties.
Continuous cooling transformation diagrams in synthetic weld heat-affected zone (SH-CCT diagrams) show the phase transition temperature and hardness at different cooling rates, which is an important basis for formulating the welding process or predicting the performance of welding heat-affected zone. However, the experimental determination of SH-CCT diagrams is a time-consuming and costly process, which does not conform to the development trend of new materials. In addition, the prediction of SH-CCT diagrams using metallurgical models remains a challenge due to the complexity of alloying elements and welding processes. So, in this study, a hybrid machine learning model consisting of multilayer perceptron classifier, k-Nearest Neighbors and random forest is established to predict the phase transformation temperature and hardness of low alloy steel using chemical composition and cooling rate. Then the SH-CCT diagrams of 6 kinds of steels are calculated by the hybrid machine learning model. The results show that the accuracy of the classification model is up to 100%, the predicted values of the regression models are in good agreement with the experimental results, with high correlation coefficient and low error value. Moreover, the mathematical expressions of hardness in welding heat-affected zone of low alloy steel are calculated by symbolic regression, which can quantitatively express the relationship between alloy composition, cooling time and hardness. This study demonstrates the great potential of the material informatics in the field of welding technology.
With the rapid development of artificial intelligence technology and increasing material data, machine learning- and artificial intelligence-assisted design of high-performance steel materials is ...becoming a mainstream paradigm in materials science. Machine learning methods, based on an interdisciplinary discipline between computer science, statistics and material science, are good at discovering correlations between numerous data points. Compared with the traditional physical modeling method in material science, the main advantage of machine learning is that it overcomes the complex physical mechanisms of the material itself and provides a new perspective for the research and development of novel materials. This review starts with data preprocessing and the introduction of different machine learning models, including algorithm selection and model evaluation. Then, some successful cases of applying machine learning methods in the field of steel research are reviewed based on the main theme of optimizing composition, structure, processing, and performance. The application of machine learning methods to the performance-oriented inverse design of material composition and detection of steel defects is also reviewed. Finally, the applicability and limitations of machine learning in the material field are summarized, and future directions and prospects are discussed.
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