Revealing the role of pyramidal slip in the high ductility of Mg-Li alloy

The high ductility of Mg-Li alloy has been mainly ascribed to a high activity of pyramidal<c+a>slip to accommodate plastic strain.In the present study,however,a quantitative analysis reveals that Li-addition can only slightly stimulate the activation of pyramidal<c+a>slip under compression along the normal direction of a hot-rolled Mg-4.5 wt.%Li plate,with a relative activity of approximately 18%.Although the limited activity of pyramidal<c+a>slip alone cannot accommodate a large plastic strain,it effectively reduces the number of{10.11}−{10.12}double twins,which are believed to be favorable sites for crack initiation.The evidently reduced activity of double twins leads to a lower cracking tendency,and therefore improves ductility.

Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms

The stress-life curve(S–N)and low-cycle strain-life curve(E–N)are the two primary representations used to characterize the fatigue behavior of a material.These material fatigue curves are essential for structural fatigue analysis.However,conducting material fatigue tests is expensive and time-intensive.To address the challenge of data limitations on ferrous metal materials,we propose a novel method that utilizes the Random Forest Algorithm and transfer learning to predict the S–N and E–N curves of ferrous materials.In addition,a data-augmentation framework is introduced using a conditional generative adversarial network(cGAN)to overcome data deficiencies.By incorporating the cGAN-generated data,the accuracy(R2)of the Random Forest Algorithm-trained model is improved by 0.3–0.6.It is proven that the cGAN can significantly enhance the prediction accuracy of the machine-learning model and balance the cost of obtaining fatigue data from the experiment.

Mg-3Al-1Zn alloy deformed along different strain paths: Role of latent hardening

The mechanical properties of magnesium alloy AZ31 were investigated experimentally with visco-plastic self-consistent modeling. Tension,compression and plane strain compression(PSC) tests were performed along 3 directions of a hot rolled plate, and the material parameters input in the model were fitted with the uniaxial stress-strain curves. The critical resolved shear stress(CRSS) for tension twinning was modeled with a modified Voce hardening law first decreasing, and then increasing with strain, that could reproduce better the flow stress for twin-predominant deformation. Such CRSS evolution may better model twin nucleation, propagation and growth. Firstly simulations were carried out assuming latent hardening coefficients for slip by other slip systems equal to self-hardening. Then different heterogeneous latent hardening were used, whose values were based on dislocation dynamics simulations from the literature. This study shows that equal self and latent hardening can reproduce the stress strain curves and plastic anisotropy as well as heterogeneous mode on mode latent hardening.Discrepancies between simulations and experimental results from PSC are explained by an under-estimation of twinning for some PSC strain paths.

Four new orientation relationships of Mg_(17)Al_(12) phase with magnesium matrix in Mg-8Al-0.5Zn alloys

Although the non-basal precipitates, those not parallel to the basal plane, are more effective to block basal slip in Mg-Al alloys, the crystallographic orientation relationship(OR) between these precipitates and the α-Mg matrix has not been well established. In this work, the crystallography of the non-basal Mg_(17)Al_(12) precipitates in AZ80 alloy was systematically investigated by transmission electron microscopy(TEM). By tilting to a suitable electron beam direction, different kinds of non-basal precipitates were recognized in TEM, and the following four new ORs between the non-basal Mg_(17)Al_(12) precipitates and the matrix were revealed: ■, and ■.Furthermore, these ORs and their habit planes were explained using the edge-to-edge matching model. The findings in this work can provide some guidelines for designing the microstructure of Mg-Al alloys to enhance their precipitation hardening potential.

Effect of pre-straining on structure and formation mechanism of precipitates in Al−Mg−Si−Cu alloy

The effect of pre-straining on the structure and formation mechanism of precipitates in an Al−Mg−Si−Cu alloy was systematically investigated by atomic resolution high-angle annular dark-field scanning transmission electron microscopy(HAADF-STEM).Elongated and string-like precipitates are formed along the dislocations in the pre-strained Al−Mg−Si−Cu alloy.The precipitates formed along the dislocations exhibit three features:non-periodic atomic arrangement within the precipitate;Cu segregation occurring at the precipitate/α(Al)interface;different orientations presented in one individual precipitate.Four different formation mechanisms of these heterogeneous precipitates were proposed as follows:elongated precipitates are formed independently in the dislocation;string-like precipitates are formed directly along the dislocations;different precipitates encounter to form string-like precipitates;precipitates are connected by other phases or solute enrichment regions.These different formation mechanisms are responsible for forming different atomic structures and morphologies of precipitates.

Recent Progress of Synchrotron X-Ray Imaging and Diffraction on the Solidification and Deformation Behavior of Metallic Materials

Characterizing the microstructure and deformation mechanism associated with the performances and properties of metallic materials is of great importance in understanding the microstructure-property relationship.The past few decades have witnessed the rapid development of characterization techniques from optical microscopy to electron microscopy,although these conventional methods are generally limited to the sample surface because of the intrinsic opaque nature of metallic materials.Advanced synchrotron radiation(SR)facilities can produce X-rays with strong penetrability and high spatiotemporal resolution,and thereby enabling the non-destructive visualization of full-field structural information in three dimensions.Tremendous endeavors were devoted to the 3 rd generation SR over the past three decades,in which X-ray beams have been focused down to 100 nm.In this paper,recent progresses on SR-related characterization technologies were reviewed,with particular emphases on the fundamentals of synchrotron X-ray imaging and synchrotron X-ray diffraction,as well as their applications in the in situ observations of material preparation(e.g.,in situ dendrite growth during solidification)and service under extreme environment(e.g.,in situ mechanics).Future innovations toward next-generation SR and newly emerging SRbased technologies such as dark-field X-ray microscopy and Bragg coherent X-ray diffraction imaging were also advocated.

Quantitative study on the tension-compression yield asymmetry of a Mg-3Al-1Zn alloy with bimodal texture components

This study demonstrates the yield asymmetry in Mg-3Al-1Zn alloy containing both ND-texture(c-axis//ND(Normal direction))and TD-texture(c-axis//TD(Transverse direction))in a quantitative view.The results showed that the yield asymmetry is strongly dependent on the distribution of bimodal texture components,on the basis of the successful establishment of the quantified relationship between pre-deformation parameters and texture components distribution.It’s meaningful for providing key reference to texture design.Mechanical behavior of bimodal textured Mg alloy under tension and compression was tested.CYS/TYS(compressive yield stress/tensile yield stress)equal to 1 is obtained,implying that the yield asymmetry is eliminated when two textures distribute at specific fractions.The corresponding mechanism for the texture-dependence of tension-compression yield asymmetry is revealed by the analysis of slip/twinning activities and a compound use of the activation stress difference of slip/twinning(ΔStress)and geometrical compatibility factor(m′)between neighboring grains.Balanced activity of{10■2}twinning and a quite similar boundary obstacle effect against slip/twinning transfer under tension and compression accounts for such good symmetry performance.

Micro Hierarchical Structure and Mechanical Property of Sparrow Hawk (Accipiter nisus) Feather Shaf

In this study,the real 3D model of the feather shaft that is composed of medulla and cortex is characterized by X-ray computer tomography,and the structural features are quantitatively analyzed.Compression and tensile tests are conducted to evaluate the mechanical performance of the feather shaft and cortex at different regions.The analysis of the 3D model shows that the medulla accounts for∼70%of the shaft volume and exhibits a closed-cell foam-like structure,with a porosity of 59%.The cells in the medulla show dodecahedron and decahedron morphology and have an equivalent diameter of∼30μm.In axial compression,the presence of medulla enhances the shaft stability.Especially,the combined effect of the medulla and cortex increases the buckling strength of the middle and distal shaft by 77%and 141%,respectively,compared to the calculated value of the shaft using linear mixed rule.The tensile properties of the cortex along the shaft axis are anisotropic because of the different fiber structures.As the fiber orientation gradually becomes uniform in the axial direction,the Young’s modulus and tensile strength of the cortex on the dorsal gradually increase from calamus to the distal shaft,and the fracture mode changes from tortuous fracture to V-shaped fracture.The cortex on the lateral shows the opposite trend,that is the distal shaft becomes weaker due to fiber tangles.

Al-enabled properties distribution prediction for high-pressure die casting Al-Si alloy

High-pressure die casting(HPDC)is one of the most popular mass production processes in the automotive industry owing to its capability for part consolidation.However,the nonuniform distribution of mechanical properties in large-sized HPDC products adds complexity to part property evaluation.Therefore,a methodology for property prediction must be developed.Material characterization,simulation technologies,and artificial intelligence(AI)algorithms were employed.Firstly,an image recognition technique was employed to construct a temperature-microstructure characteristic model for a typical HPDC Al7Si0.2Mg alloy.Moreover,a porosity/microstructure-mechanical property model was established using a machine learning method based on the finite element method and representative volume element model results.Additionally,the computational results of the casting simulation software were mapped with the porosity/microstructure-mechanical property model,allowing accurate prediction of the property distribution of the HPDC Al-Si alloy.The AI-enabled property distribution model developed in this study is expected to serve as a foundation for intelligent HPDC part design platforms in the automotive industry.

Detwinning and Anneal-Hardening Behaviors of Pre-Twinned AZ31 Alloys under Cryogenic Loading

Detwinning behavior of a pre-twinned magnesium alloy AZ31 at cryogenic temperature was investigated,also with a focus on the annealing hardening behavior of samples with different fractions of pre-twins.Pre-compression along the transverse direction with strains of 1.7%,3.0%,and 6.0% was applied to generated[1012]twins.Mechanical behavior,microstructure,and texture evolution during subsequent tension were examined.Our results show that low temperature did not change the fact that detwinning still pre-dominated in the pre-twinned samples under reverse loading.However,a relatively harder migration of twin boundaries was found at cryogenic temperature.An annealing hardening of 27-40 MPa was observed in the pre-twinned samples,and such a hardening effect shows a close relation with the fraction of pre-twins or the level of pre-strains.The annealing hardening effect disappeared if the matrix was consumed by twins along with the increased pre-compression strains.The corresponding reasons for the annealing hardening behavior were discussed.

Microstructure and texture evolution of Cu-Cr-Co-Ti alloys during the two-stage cryorolling

This study entailed an investigation of the mechanical properties,microstructural and texture orientation evolutions of Cu-Cr-Co-Ti alloys prepared via twostage cryorolling and intermediate aging treatment.To this end,X-ray diffraction and electron backscatter diffraction were employed.The results indicate that the two-stage cryorolling and intermediate aging treatments led to the development of profuse twin bundles and significantly enhanced the mechanical properties.The initial cryorolling led to coplanar slip and developed a strong Y({111}<112>)orientation,accelerating the formation of Goss({011}<100>)orientation and a Brass-type texture.The intermediate aging treatment relieved the restriction on dislocation slip and reoriented the grains toward the Copper({112}<111>)and Z({111}<110>)orientations.The Z orientation,with a relatively high volume fraction,dominated the macrotexture.Secondary cryorolling intensified twinning and shear banding,transforming the Copper-type shear bands into Brass-type shear bands with rhomboidal prism morphology.The areas inside the Brasstype shear bands exhibited a Y orientation,and the areas outside the shear bands exhibited a stable Brass-type texture.The evident decrease in the weighted Schmid factors demonstrated that the two-stage cryorolling and intermediate aging treatment can modify the texture evolution and aid the design of high-performance Cu alloys.

Predicting fatigue life of automotive adhesive bonded joints:a data-driven approach using combined experimental and numerical datasets

The majority of vehicle structural failures originate from joint areas.Cyclic loading is one of the primary factors in joint failures,making the fatigue performance of joints a critical consideration in vehicle structure design.The use of traditional fatigue analysis methods is constrained by the absence of adhesive life data and the wide variety of joint geometries.Therefore,there is a pressing need for an accurate fatigue life estimation method for the joints in the automotive industry.In this work,we proposed a data-driven approach embedding physical knowledge-guided parameters based on experimental data and finite element analysis(FEA)results.Different machine learning(ML)algorithms are adopted to investigate the fatigue life of three typical adhesive joints,namely lap shear,coach peel and KSII joints.After the feature engineering and tuned process of the ML models,the preferable model using the Gaussian process regression algorithm is established,fed with eight input parameters,namely thicknesses of the substrates,line forces and bending moments of the adhesive bonded joints obtained from FEA.The proposed method is validated with the test data set and part-level physical tests with complex loading states for an unbiased evaluation.It demonstrates that for life prediction of adhesive joints,the data-driven solutions can constitute an improvement over conventional solutions.

A data-driven approach for predicting the fatigue life and failure mode of self-piercing rivet joints

In lightweight automotive vehicles,the application of self-piercing rivet(SPR)joints is becoming increasingly widespread.Considering the importance of automotive service performance,the fatigue performance of SPR joints has received considerable attention.Therefore,this study proposes a data-driven approach to predict the fatigue life and failure modes of SPR joints.The dataset comprises three specimen types:cross-tensile,cross-peel,and tensile-shear.To ensure data consistency,a finite element analysis was employed to convert the external loads of the different specimens.Feature selection was implemented using various machine-learning algorithms to determine the model input.The Gaussian process regression algorithm was used to predict fatigue life,and its performance was compared with different kernel functions commonly used in the field.The results revealed that the Matern kernel exhibited an exceptional predictive capability for fatigue life.Among the data points,95.9%fell within the 3-fold error band,and the remaining 4.1%exceeded the 3-fold error band owing to inherent dispersion in the fatigue data.To predict the failure location,various tree and artificial neural network(ANN)models were compared.The findings indicated that the ANN models slightly outperformed the tree models.The ANN model accurately predicts the failure of joints with varying dimensions and materials.However,minor deviations were observed for the joints with the same sheet.Overall,this data-driven approach provided a reliable predictive model for estimating the fatigue life and failure location of SPR joints.

Understanding the Mechanism for the In-Plane Yielding Anisotropy of a Hot-Rolled Zirconium Plate

Previously,the in-plane mechanical anisotropy of Zr hot-rolled plates is ascribed mainly to the different activities of the deformation modes activated when loading along different directions.In this work,a quantitative study on the deformation behavior of a pure Zr hot-rolled plate under tension along the rolling direction(RD)and transverse direction(TD)reveals that both the activities of deformation modes and the anisotropy of grain boundary strengthening account for a tensile yield strength anisotropy along the TD and RD.Crystal plasticity simulations using viso-plastic self-consistent model show that prismatic slip is the predominant deformation mode for tension along the RD(RD-tension),while prismatic slip and basal slip are co-dominant deformation modes under tension along the TD(TD-tension).A low fraction of■under TD-tension,while hardly activated under RD-tension.The activation of basal slip with a much higher critical resolve shear stress under TD-tension contributes to a higher yield strength along the TD than along the RD.The grain boundary strengthening effect under tension along the TD and RD were compared by calculating the activation stress difference(△Stress)and the geometric compatibility factor(m′)between neighboring grains.The results indicate a higher grain boundary strengthening for TD-tension than that for RD-tension,which will lead to a higher yield strength along the TD.That is,the anisotropy of grain boundary strengthening between TD-tension and RD-tension also plays an important role in the in-plane anisotropy along the RD and TD.Afterward,the reasons for why there is a grain-boundary-strengthening anisotropy along the TD and RD were discussed.

Improving RSW nugget diameter prediction method:unleashing the power of multi-fidelity neural networks and transfer learning

This research presents an innovative approach to accurately predict the nugget diameter in resistance spot welding(RSW)by leveraging machine learning and transfer learning methods.Initially,low-fidelity(LF)data were obtained through finite element numerical simulation and design of experiments(DOEs)to train the LF machine learning model.Subsequently,high-fidelity(HF)data were collected from RSW process experiments and used to fine-tune the LF model by transfer learning techniques.The accuracy and generalization performance of the models were thoroughly validated.The results demonstrated that combining different fidelity datasets and employing transfer learning could significantly improve the prediction accuracy while minimize the costs associated with experimental trials,and provide an effective and valuable method for predicting critical process parameters in RSW.

A machine learning-based calibration method for strength simulation of self-piercing riveted joints

This paper presents a new machine learning-based calibration framework for strength simulation models of self-piercing riveted(SPR)joints.Strength simulations were conducted through the integrated modeling of SPR joints from process to performance,while physical quasi-static tensile tests were performed on combinations of DP600 high-strength steel and 5754 aluminum alloy sheets under lap-shear loading conditions.A sensitivity study of the critical simulation parameters(e.g.,friction coefficient and scaling factor)was conducted using the controlled variables method and Sobol sensitivity analysis for feature selection.Subsequently,machine-learning-based surrogate models were used to train and accurately represent the mapping between the detailed joint profile and its load-displacement curve.Calibration of the simulation model is defined as a dual-objective optimization task to minimize errors in key load displacement features between simulations and experiments.A multi-objective genetic algorithm(MOGA)was chosen for optimization.The three combinations of SPR joints illustrated the effectiveness of the proposed framework,and good agreement was achieved between the calibrated models and experiments.

Atomistic Investigation of Shock-Induced Amorphization within Micro-shear Bands in Hexagonal Close-Packed Titanium

The mechanical response of a single crystal titanium sample against(0001)α surface impact was investigated using molecular dynamics simulation.Remarkably,non-uniform plastic deformation was observed in the sample.At high strain rates,amorphization occurred near the edge of the contact region where severe shear strain induced a large number of stacking faults(SFs)and dislocations.In contrast,the central part of the contact region underwent less deformation with significantly fewer dislocations.Moreover,instead of amorphization by consuming SFs and dislocations,there was a gradual increase in the density of dislocations and SFs during the process of amorphization.These local amorphous regions eventually grew into shear bands.

Ductile and high strength Cu fabricated by solid-state cold spray additive manufacturing

In this work,pure Cu with excellent strength and ductility(UTS of 271 MPa,elongation to fracture of 43.5%,uniform elongation of 30%)was prepared using cold spray additive manufacturing(CSAM),realizing a breakthrough in the field.An in-depth investigation was conducted to reveal the microstructure evolution,strengthening and ductilization mechanisms of the CSAM Cu,as well as the single splats.The results show that the CSAM Cu possesses a unique heterogeneous microstructure with a bimodal grain structure and extensive infinitely circulating ring-mounted distribution of twinning.Based on the single splat observation,the entire copper particle forms a gradient nano-grained(GNG)structure after high-speed impact deposition.The GNG-structured single splat serves as a unit to build the heterogeneous microstructure with bimodal grain distribution during the successive deposition in CSAM.The results also show that CSAM can achieve synergistic strengthening and ductilization by controlling the grain refinement and dislocation density.This work provides potential for CSAM technique in manufacturing various metallic parts with the desired combination of high strength and good ductility without additional post-treatments.

Effect of microstructure evolution on wear resistance of equal molar CoCrFeNi high-entropy alloy

Face-centered-cubic high-entropy alloys have excellent ductility and great potential for engineering applications.However,few studies have reported on the wear behavior under different loads.This study aimed to investigate the tribological behavior of CoCrFeNi highentropy alloy under different loads.The wear rate increased with the load,and the main wear behavior was abrasive wear.The cross-sectional microstructure of worn surface consisted of oxide,nanocrystalline,nanolaminated,and deformation layers.Under the higher loads,the deformation mechanism included dislocation slip and deformation twins,and shear bands also appeared.It was unexpectedly found that the wear rate increased sharply under high load,which was attributed to the formation of shear bands and microcracks in the nanocrystalline layer.The results successfully revealed the great influence of microstructure evolution on the wear rate,providing theoretical guidance for improving the wear resistance of face-centered-cubic high-entropy alloys in the future.

On the modeling of deformation mechanisms in a Mg-3Al-1Zn alloy under biaxial tension

Although the{10-12}twinning behavior of Mg alloys under uniaxial tension and compression has been extensively investigated,the simulations of{10-12}twinning behavior under biaxial tension have rarely been reported.In this work,the EVPSC-TDT model is first employed to systematically investigate the deformation behavior of a Mg alloy AZ31 plate under biaxial tension in the RD-TD and ND-TD planes.The RD,TD and ND refer to the rolling direction,transverse direction,and normal direction of the hot rolled plate.The measured stress-strain curves and texture evolutions are well predicted and the con-tours of plastic work under biaxial tension are also constructed for comparison with experiments.The plastic response has been interpreted in terms of relative activities of various deformation modes.For bi-axial tension in the RD-TD plane,basal and pyramidal slips mainly contribute to the plastic deformation for stress ratios ofσRD:σTD=1:2 to 2:1.Prismatic slip becomes more active forσRD:σTD=1:4 and 4:1.Compression twinning could be activated and so cause texture reorientation at large strains,especially forσRD:σTD=1:1.The six-fold feature of{10-10}pole figure could still be observed forσRD:σTD=1:4 and 4:1 at large strain.For biaxial tension in the ND-TD plane,tensile twinning plays an important role forσND:σTD≥1:2,while prismatic slip contributes to plastic deformation for the other cases.With the in-crease of stress ratio fromσND:σTD≥1:1 to 1:0,the predicted twin volume fractions(VFs)at a specific strain along the ND,εND,almost linearly decrease,however,it is seen that the experimental ones at given strains along the ND do not follow such a trend with the measured twin VFs within the range of stress ratios,2:1≤σND:σTD≤6:1,clearly being overestimated,and the difference between experiments and simulations becomes most obvious at the relatively small strain ofεND=0.015.The possible reasons for the observed difference are discussed.