Review of precipitation strengthening in ultrahigh-strength martensitic steel

Martensite is an important microstructure in ultrahigh-strength steels,and enhancing the strength of martensitic steels often involves the introduction of precipitated phases within the martensitic matrix.Despite considerable research efforts devoted to this area,a systematic summary of these advancements is lacking.This review focuses on the precipitates prevalent in ultrahigh-strength martensitic steel,primarily carbides(e.g.,MC,M_(2)C,and M_(3)C)and intermetallic compounds(e.g.,Ni Al,Ni_(3)X,and Fe_(2)Mo).The precipitation-strengthening effect of these precipitates on ultrahigh-strength martensitic steel is discussed from the aspects of heat treatment processes,microstructure of precipitate-strengthened martensite matrix,and mechanical performance.Finally,a perspective on the development of precipitation-strengthened martensitic steel is presented to contribute to the advancement of ultrahigh-strength martensitic steel.This review highlights significant findings,ongoing challenges,and opportunities in the development of ultrahigh-strength martensitic steel.

Effect of deformation parameters on the austenite dynamic recrystallization behavior of a eutectoid pearlite rail steel

Understandings of the effect of hot deformation parameters close to the practical production line on grain refinement are crucial for enhancing both the strength and toughness of future rail steels.In this work,the austenite dynamic recrystallization(DRX)behaviors of a eutectoid pearlite rail steel were studied using a thermo-mechanical simulator with hot deformation parameters frequently employed in rail production lines.The single-pass hot deformation results reveal that the prior austenite grain sizes(PAGSs)for samples with different deformation reductions decrease initially with an increase in deformation temperature.However,once the deformation temperature is beyond a certain threshold,the PAGSs start to increase.It can be attributed to the rise in DRX volume fraction and the increase of DRX grain with deformation temperature,respectively.Three-pass hot deformation results show that the accumulated strain generated in the first and second deformation passes can increase the extent of DRX.In the case of complete DRX,PAGS is predominantly determined by the deformation temperature of the final pass.It suggests a strategic approach during industrial production where part of the deformation reduction in low temperature range can be shifted to the medium temperature range to release rolling mill loads.

Tribological properties of high-entropy alloys:A review

Tribology,which is the study of friction,wear,and lubrication,largely deals with the service performance of structural materials.For example,newly emerging high-entropy alloys(HEAs),which exhibit excellent hardness,anti-oxidation,anti-softening ability,and other prop-erties,enrich the wear-resistance alloy family.To demonstrate the tribological behavior of HEAs systematically,this review first describes the basic tribological characteristics of single-,dual-,and multi-phase HEAs and HEA composites at room temperature.Then,it summarizes the strategies that improve the tribological property of HEAs.This review also discusses the tribological performance at elevated temperatures and provides a brief perspective on the future development of HEAs for tribological applications.

Titanium microalloying of steel:A review of its effects on processing,microstructure and mechanical properties

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.

Advances in machine learning-and artificial intelligence-assisted material design of steels

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.

Recent research progress on the phase-field model of microstructural evolution during metal solidification

Solidification structure is a key aspect for understanding the mechanical performance of metal alloys,wherein composition and casting parameters considerably influence solidification and determine the unique microstructure of the alloys.By following the principle of free energy minimization,the phase-field method eliminates the need for tracking the solid/liquid phase interface and has greatly accelerated the research and development efforts geared toward optimizing metal solidification microstructures.The recent progress in the application of phasefield simulation to investigate the effect of alloy composition and casting process parameters on the solidification structure of metals is summarized in this review.The effects of several typical elements and process parameters,including carbon,boron,silicon,cooling rate,pulling speed,scanning speed,anisotropy,and gravity,on the solidification structure are discussed.The present work also addresses the future prospects of phase-field simulation and aims to facilitate the widespread applications of phase-field approaches in the simulation of microstructures during solidification.

Polymer-/Ceramic-based Dielectric Composites for Energy Storage and Conversion

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 highperformance 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.

First-principle study on the segregation and strengthening behavior of solute elements at grain boundary in BCC iron

Grain boundary(GB)significantly influences the mechanical properties of metal structural materials,yet the effect of solutes on GB modification and the underlying atomic mechanisms of solute segregation and strengthening in iron-based alloys remain insufficiently explored.To address this research gap,we conducted a comprehensive investigation into the segregation and strengthening effect of 33 commonly occurring solutes in iron-based alloys,with a specific focus on the body-centered cubic(BCC)iron5(310)GB,utilizing first-principle calculations.Our findings reveal a negative linear correlation between solute segregation energy and atomic radius,highlighting the crucial role of atomic radius and electronic structure in determining GB strength.Moreover,through analyzing the relationship between strengthening energy and segregation energy,it was found that the elements Ni,Co,Ti,V,Mn,Nb,Cr,Mo,W,and Re are significant enhancers of GB strength upon segregation.This study aims to provide theoretical guidance for selecting optimal doping elements in BCC iron-based alloys.

Oxidation behavior of Mo-Si-B alloys at medium-to-high temperatures

Continuous exploration of high-temperature structural materials is being driven by the needs of gasturbine engines capable of withstanding the high-temperature environment.Relatively low melting points of currently applied superalloys restrain the further improvement of service tempe ratures.With higher melting tempe ratures above 2000℃,Mo-Si-B alloys are regarded as a new generation of ultrahightemperature structural materials.However,oxidation is a concern for the industrial application of Mo-Si-B alloys.Therefore,an in-depth understanding of the oxidation mechanisms may contribute to solving this issue,whereas relevant reviews about their recent advances are lacking.In the current work,a comprehensively systematic review about the oxidation behaviors of Mo-Si-B alloys is described for this purpose.

Atomic-scale simulations in multi-component alloys and compounds:A review on advances in interatomic potential

Multi-component alloys have demonstrated excellent performance in various applications,but the vast range of possible compositions and microstructures makes it challenging to identify optimized alloys for specific purposes.To overcome this challenge,large-scale atomic simulation techniques have been widely used for the design and optimization of multi-component alloys.The capability and reliability of large-scale atomic simulations essentially rely on the quality of interatomic potentials that describe the interactions between atoms.This work provides a comprehensive summary of the latest advances in atomic simulation techniques for multi-component alloys.The focus is on interatomic potentials,including both conventional empirical potentials and newly developed machine learning potentials(MLPs).The fitting processes for different types of interatomic potentials applied to multi-component alloys are also discussed.Finally,the challenges and future perspectives in developing MLPs are thoroughly addressed.Overall,this review provides a valuable resource for researchers interested in developing optimized multicomponent alloys using atomic simulation techniques.

A hybrid machine learning model for predicting continuous cooling transformation diagrams in welding heat-affected zone of low alloy steels

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 SHCCT 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.

Martensitic twinning transformation mechanism in a metastable IVB element-based body-centered cubic high-entropy alloy with high strength and high work hardening rate

Realizing high work hardening and thus elevated strength–ductility synergy are prerequisites for the practical usage of body-centered-cubic high entropy alloys(BCC-HEAs).In this study,we report a novel dynamic strengthening mechanism,martensitic twinning transformation mechanism in a metastable refractory element-based BCC-HEA(TiZrHf)Ta(at.%)that can profoundly enhance the work hardening capability,leading to a large uniform ductility and high strength simultaneously.Different from conventional transformation induced plasticity(TRIP)and twinning induced plasticity(TWIP)strengthening mechanisms,the martensitic twinning transformation strengthening mechanism combines the best characteristics of both TRIP and TWIP strengthening mechanisms,which greatly alleviates the strengthductility trade-off that ubiquitously observed in BCC structural alloys.Microstructure characterization,carried out using X-ray diffraction(XRD)and electron back-scatter diffraction(EBSD)shows that,upon straining,α”(orthorhombic)martensite transformation,self-accommodation(SA)α”twinning and mechanicalα”twinning were activated sequentially.Transmission electron microscopy(TEM)analyses reveal that continuous twinning activation is inherited from nucleating mechanical{351}type I twins within SA“{351}”<■11>typeⅡtwinnedα”variants on{351}twinning plane by twinning transformation through simple shear,thereby accommodating the excessive plastic strain through the twinning shear while concurrently refining the grain structure.Consequently,consistent high work hardening rates of 2–12.5 GPa were achieved during the entire plastic deformation,leading to a high tensile strength of 1.3 GPa and uniform elongation of 24%.Alloy development guidelines for activating such martensitic twinning transformation strengthening mechanism were proposed,which could be important in developing new BCC-HEAs with optimal mechanical performance.

Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning

High toughness is highly desired for low-alloy steel in engineering structure applications,wherein Charpy impact toughness(CIT)is a critical factor determining the toughness performance.In the current work,CIT data of low-alloy steel were collected,and then CIT prediction models based on machine learning(ML)algorithms were established.Three feature construction strategies were proposed.One is solely based on alloy composition,another is based on alloy composition and heat treatment parameters,and the last one is based on alloy composition,heat treatment parameters,and physical features.A series of ML methods were used to effectively select models and material descriptors from a large number of al-ternatives.Compared with the strategy solely based on the alloy composition,the strategy based on alloy composition,heat treatment parameters together with physical features perform much better.Finally,a genetic programming(GP)based symbolic regression(SR)approach was developed to establish a physical meaningful formula between the selected features and targeted CIT data.

Data-driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology

With the development of new information technology,big data technology and artificial intelligence(AI)have accelerated material research and development and industrial manufacturing,which have become the key technology driving a new wave of global technological revolution and industrial transformation.This review introduces the data resources and databases related to steel materials.It then examines the fundamental strategies and applications of machine learning(ML)in the design and discovery of steel materials,including ML models based on experimental data,industrial manufacturing data,and simulation data,respectively.Given the advancements in big data,AI/ML,and new communication technologies,an intelligent manufacturing mode featuring digital twins is deemed critical in guiding the next industrial revolution.Consequently,the application of intelligence manufacturing with digital twins in the iron and steel industry is reviewed and discussed.Furthermore,the applications of ML in service performance prediction of steel products are addressed.Finally,the future development trends for datadriven and AI approaches throughout the entire life cycle of steel materials are prospected.Overall,this work presents an in-depth examination of the integration of datadriven and AI technologies in the steel industry,highlighting their potential and future directions.

Perspective on antiferroelectrics for energy storage and conversion applications

As a close relative of ferroelectricity,antiferroelectricity has received a recent resurgence of interest driven by technological aspirations in energy-efficient applications,such as energy storage capacitors,solid-state cooling devices,explosive energy conversion,and displacement transducers.Though prolonged efforts in this area have led to certain progress and the discovery of more than 100 antiferroelectric materials over the last 70 years,some scientific and technological issues remain unresolved.Herein,we provide perspectives on the development of antiferroelectrics for energy storage and conversion applications,as well as a comprehensive understanding of the structural origin of antiferroelectricity and field-induced phase transitions,followed by design strategies for new lead-free antiferroelectrics.We also envision unprecedented challenges in the development of promising antiferroelectric materials that bridge materials design and real applications.Future research in these directions will open up new possibilities in resolving the mystery of antiferroelectricity,provide opportunities for comprehending structure-property correlation and developing antiferroelectric/ferroelectric theories,and suggest an approach to the manipulation of phase transitions for real-world applications.

Distribution behavior of inclusions in compact-strip-produced martensitic steel and its influence on mechanical properties

The distribution behavior of inclusions in martensitic steel produced by compact strip production process(CSP-MS)and its influence on mechanical properties were systematically investigated.The inclusions in the CSP-MS specimen are mainly composed of spherical Al_(2)O_(3)-CaO-CaS,MnS with high aspect ratio and small-sized TiN,whereas many coarse cuboidal TiN inclusions do exist in conventional martensitic steel(Con-MS).The high inclusion density of the CSP-MS specimen resulted in lower total elongation and impact toughness,and the MnS inclusions with high aspect ratio led to significantly stronger mechanical anisotropy than that for Con-MS specimen.The in-situ tensile results indicated that when the fracture direction is parallel to MnS direction,the microcracks induced by MnS inclusions tend to propagate into the matrix,leading to the formation of valley-like features,which significantly deteriorate the properties such as the total elongation and impact toughness.The microcracks caused by TiN inclusions are sharper than those caused by spherical Al_(2)O_(3)-CaO-CaS inclusions,and are easy to propagate into the matrix.This work is expected to guide the optimization of the mechanical properties of martensitic steels produced by CSP process and provide a theoretical basis for the CSP process design.

Enhanced Hydrogen Embrittlement Resistance via Cr Segregation in Nanocrystalline Fe-Cr Alloys

Hydrogen is a clean fuel with numerous sources,yet the hydrogen industry is plagued by hydrogen embrittlement(HE)issues during the storage,transportation,and usage of hydrogen gas.HE can compromise material performance during service,leading to significant safety hazards and economic losses.In the current work,the influence of element Cr on the HE resistance of nanocrystalline Fe-Cr alloys under different hydrogen concentrations and strain rates was evaluated.With hybrid Monte Carlo(MC)and molecular dynamics(MD)simulations,it was found that Cr atoms were segregated at grain boundaries(GB)and inhibited the GB decohesion.Correspondingly,Cr segregation improved the strength and plasticity of the nanocrystalline Fe-Cr alloys,especially the HE resistance.Moreover,the Cr segregation reduced the diffusion coefficient of hydrogen and inhibited hydrogen-induced cracking.This work provided new insight into the development of iron-based alloys with high HE resistance in the future.

Towards enhanced strength-ductility synergy via hierarchical design in steels:from the material genome perspective

Steels, accounting for a large proportion of metals and their alloys, are still irreplaceable structural materials in industrial applications for a long time. Classical dislocation theory sheds light that strength can be enhanced by impeding the dislocation motion.Meanwhile, the ductility depends on the mobility of dislocation movement. As a result, one method of increasing strength or ductility always damages the other. In other words, there is a mutual exclusion between strength and ductility from the perspective of dislocation movement [1].