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.

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.

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.

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.

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.

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.

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.

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.