Combining gradient structure and supersaturated solid solution to achieve superior mechanical properties in WE43 magnesium alloy

In this study,surface mechanical attrition treatment was employed to sucessfully produce a gradient nanostructured layer on WE43 magnesium alloy.X-ray diffraction,energy dispersive X-ray spectrometer,and high-resolution transmission electron microscope observations were mainly performed to uncover the microstructure evolution responsible for the refinement mechanisms.It reveals that the grain refinement process consists of three transition stages along the depth direction from the core matrix to the topmost surface layer,i.e.,dislocation cells and pile-ups,ultrafine subgrains,and randomly orientated nanograins with the grain size of~40 nm.Noticeably,the original Mg;RE second phase is also experienced refinement and then re-dissolved into the α-Mg matrix phase,forming a supersaturated solid solution nanostructuredα-Mg phase in the gradient refined layer.Due to the cooperative effects of grain refinement hardening,dislocation hardening,and supersaturated solid-solution hardening,the gradient nanostructured WE43 alloy contributes to the ultimate tensile strength of~435 MPa and ductility of~11.0%,showing an extraordinary strain hardening and mechanical properties among the reported severe plastic deformation-processed Mg alloys.This work provides a new strategy for the optimization of mechanical properties of Mg alloys via combining the gradient structure and supersaturated solid solution.

Phase field modeling for the morphological and microstructural evolution of metallic materials under environmental attack

The complex degradation of metallic materials in aggressive environments can result in morphological and microstructural changes.The phase-field(PF)method is an effective computational approach to understanding and predicting the morphology,phase change and/or transformation of materials.PF models are based on conserved and non-conserved field variables that represent each phase as a function of space and time coupled with time-dependent equations that describe the mechanisms.This report summarizes progress in the PF modeling of degradation of metallic materials in aqueous corrosion,hydrogen-assisted cracking,high-temperature metal oxidation in the gas phase and porous structure evolution with insights to future applications.

Phase-field model of pitting corrosion kinetics in metallic materials

Pitting corrosion is one of the most destructive forms of corrosion that can lead to catastrophic failure of structures.This study presents a thermodynamically consistent phase field model for the quantitative prediction of the pitting corrosion kinetics in metallic materials.An order parameter is introduced to represent the local physical state of the metal within a metal-electrolyte system.The free energy of the system is described in terms of its metal ion concentration and the order parameter.Both the ion transport in the electrolyte and the electrochemical reactions at the electrolyte/metal interface are explicitly taken into consideration.The temporal evolution of ion concentration profile and the order parameter field is driven by the reduction in the total free energy of the system and is obtained by numerically solving the governing equations.A calibration study is performed to couple the kinetic interface parameter with the corrosion current density to obtain a direct relationship between overpotential and the kinetic interface parameter.The phase field model is validated against the experimental results,and several examples are presented for applications of the phase-field model to understand the corrosion behavior of closely located pits,stressed material,ceramic particles-reinforced steel,and their crystallographic orientation dependence.

Machine learning of phases and mechanical properties in complex concentrated alloys

The mechanical properties of complex concentrated alloys(CCAs)depend on their formed phases and corresponding microstructures.The data-driven prediction of the phase formation and associated mechanical properties is essential to discovering novel CCAs.The present work collects 557 samples of various chemical compositions,comprising 61 amorphous,167 single-phase crystalline,and 329 multiphases crystalline CCAs.Three classification models are developed with high accuracies to category and understand the formed phases of CCAs.Also,two regression models are constructed to predict the hardness and ultimate tensile strength of CCAs,and the correlation coefficient of the random forest regression model is greater than 0.9 for both of two targeted properties.Furthermore,the Shapley additive explanation(SHAP)values are calculated,and accordingly four most important features are identified.A significant finding in the SHAP values is that there exists a critical value in each of the top four features,which provides an easy and fast assessment in the design of improved mechanical properties of CCAs.The present work demonstrates the great potential of machine learning in the design of advanced CCAs.

Predicting surface deformation during mechanical attrition of metallic alloys

Extensive efforts have been devoted in both the engineering and scientific domains to seek new designs and processing techniques capable of making stronger and tougher materials.One such method for enhancing such damage-tolerance in metallic alloys is a surface nano-crystallization technology that involves the use of hundreds of small hard balls which are vibrated using high-power ultrasound so that they impact onto the surface of a material at high speed(termed Surface Mechanical Attrition Treatment or SMAT).However,few studies have been devoted to the precise underlying mechanical mechanisms associated with this technology and the effect of processing parameters.As SMAT is dynamic plastic deformation process,here we use random impact deformation as a means to investigate the relationship between impact deformation and the parameters involved in the processing,specifically ball size,impact velocity,ball density and kinetic energy.Using analytical and numerical solutions,we examine the size of the indents and the depths of the associated plastic zones induced by random impacts,with results verified by experiment in austenitic stainless steels.In addition,global random impact and local impact frequency models are developed to analyze the statistical characteristics of random impact coverage,together with a description of the effect of random multiple impacts,which are more reflective of SMAT.We believe that these models will serve as a necessary foundation for further,and more energy-efficient,development of such surface nano-crystalline processing technologies for the strengthening of metallic materials.