The improvement of mechanical properties must be achieved by designing and constructing more suitable microstructure,such as hierarchical microstructure.In order to significantly enhance the creep resistance of titani...The improvement of mechanical properties must be achieved by designing and constructing more suitable microstructure,such as hierarchical microstructure.In order to significantly enhance the creep resistance of titanium matrix composites(TMCs),two-scale network microstructure was constructed including the first-scale network(<150μm)with micro-TiB whisker(TiBw)reinforcement and the second-scale network(<30μm)with nano-Ti5Si3 reinforcement by powder metallurgy and in-situ synthesis.The results showed that the creep rate of the composite was remarkably reduced by an order of magnitude compared with the Ti6Al4V alloy at 550℃,600℃,650℃ under the stresses between 100 MPa and 350 MPa.Moreover,the rupture time of the composite was increased by 20 times,compared with that of the Ti6Al4 Valloy at 550℃/300 MPa.The superior creep resistance could be attributed to the hierarchical microstructure.The micro-TiBw reinforcement in the first-scale network boundary contributed to creep resistance primarily by blocking grain boundary sliding,while the nano-Ti5Si3 particle in the second-scale network boundary mainly by hindering phase boundary sliding.In addition,the nano-Ti5Si3 particle was dissolved,and precipitated with smaller size than the primary Ti5Si3.This phenomenon was attributed to Si element diffusion under high temperature and external stress,which could further continuously enhance the creep resistance.Finally,the creep rate during steady-state stage was significantly decreased,which manifested superior creep resistance of the composite.展开更多
The effects of the solid solution conditions on the microstructure and tensile properties of Al?Zn?Mg?Cu aluminum alloy were investigated by in-situ resistivity measurement, optical microscopy (OM), scanning electron ...The effects of the solid solution conditions on the microstructure and tensile properties of Al?Zn?Mg?Cu aluminum alloy were investigated by in-situ resistivity measurement, optical microscopy (OM), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and tensile test. A radial basis function artificial neural network (RBF-ANN) model was developed for the analysis and prediction of the electrical resistivity of the tested alloy during the solid solution process. The results show that the model is capable of predicting the electrical resistivity with remarkable success. The correlation coefficient between the predicted results and experimental data is 0.9958 and the relative error is 0.33%. The predicted data were adopted to construct a novel physical picture which was defined as “solution resistivity map”. As revealed by the map, the optimum domain for the solid solution of the tested alloy is in the temperature range of 465?475 °C and solution time range of 50?60 min. In this domain, the solution of second particles and the recrystallization phenomenon will reach equilibrium.展开更多
Developments of soft network materials with rationally distributed wavy microstructures have enabled many promising applications in bio-integrated electronic devices,due to their abilities to reproduce precisely nonli...Developments of soft network materials with rationally distributed wavy microstructures have enabled many promising applications in bio-integrated electronic devices,due to their abilities to reproduce precisely nonlinear mechanical properties of human tissues/organs.In practical applications,the soft network materials usually serve as the encapsulation layer and/or substrate of bio-integrated electronic devices,where deterministic holes can be utilized to accommodate hard chips,thereby increasing the filling ratio of the device system.Therefore,it is essential to understand how the hole-type imperfection affects the stretchability of soft network materialswith various geometric constructions.Thiswork presents a systematic investigation of the imperfection sensitivity of mechanical properties in soft network materials consisting of horseshoe microstructures,through combined computational and experimental studies.A factor of imperfection insensitivity of stretchability is introduced to quantify the influence of hole imperfections,as compared to the case of perfect soft network materials.Such factor is shown to have different dependences on the arc angle and normalized width of horseshoe microstructures for triangular network materials.The soft triangular and Kagome network materials,especially with the arc angle in the range of(30?,60?),are found to be much more imperfection insensitive than corresponding traditional lattice materials with straight microstructures.Differently,the soft honeycomb network materials are not as imperfection insensitive as traditional honeycomb lattice materials.展开更多
Microstructures of Cu-4.7Sn(%) alloys prepared by two-phase zone continuous casting(TZCC)technology contain large columnar grains and small grains.A compound grain structure,composed of a large columnar grain and at l...Microstructures of Cu-4.7Sn(%) alloys prepared by two-phase zone continuous casting(TZCC)technology contain large columnar grains and small grains.A compound grain structure,composed of a large columnar grain and at least one small grain within it,is observed and called as grain-covered grains(GCGs).Distribution of small grains,their numbers and sizes as well as numbers and sizes of columnar grains were characterized quantitatively by metallographic microscope.Back propagation(BP) artificial neural network was employed to build a model to predict microstructures produced by different processing parameters.Inputs of the model are five processing parameters,which are temperatures of melt,mold and cooling water,speed of TZCC,and cooling distance.Outputs of the model are nine microstructure quantities,which are numbers of small grains within columnar grains,at the boundaries of the columnar grains,or at the surface of the alloy,the maximum and the minimum numbers of small grains within a columnar grain,numbers of columnar grains with or without small grains,and sizes of small grains and columnar grains.The model yields precise prediction,which lays foundation for controlling microstructures of alloys prepared by TZCC.展开更多
An artificial neural network （ANN） model for predicting transformed mierostrueture in conventional rolling process and therrnomechanical controlled process （TMCP） is proposed. The model uses austenite grain size a...An artificial neural network （ANN） model for predicting transformed mierostrueture in conventional rolling process and therrnomechanical controlled process （TMCP） is proposed. The model uses austenite grain size and retained strain, which can be calculated by using microstructure evolution models, together with a measured cooling rate and chemical compositions as inputs and the ferrite grain size and ferrite fraction as outputs. The predicted re- suits show that the model can predict the transformed microstructure which is in good agreement with the measured one, and it is better than the empirical equations. Also, the effect of the alloying elements on transformed products has been analyzed by using the model. The tendency is the same as that in the reported articles. The model can be used further for the optimization of processing parameters, mierostructure and properties in TMCP.展开更多
The microstructure evolution and deformation mechanism of a DZ125 superalloy during high-temperature creep were studied by means of microstructure observation and creep-property tests.The results show that at the init...The microstructure evolution and deformation mechanism of a DZ125 superalloy during high-temperature creep were studied by means of microstructure observation and creep-property tests.The results show that at the initial stage of high-temperature creep,two sets of dislocations with different Burgers vectors move and meet inγmatrix channels,and react to form a quadrilateral dislocation network.Andγ′phases with raft-like microstructure are generated after the formation of dislocation networks.As creep progresses,the quadrilateral dislocation network is gradually transformed into hexagonal and quadrilateral dislocation networks.During steady stage of creep,the superalloy undergoes deformation with the mechanism that a great number of dislocations slip and climb in the matrix across the raft-likeγ′phases.At the later stage of creep,the raft-likeγ′phases are sheared by dislocations at the breakage of dislocation networks,and then alternate slip occurs,which distorts and breaks the raft-likeγ′/γphases,resulting in the accumulation of micropores at the raft-likeγ′/γinterfaces and the formation of microcracks.As creep continues,the microcracks continue to expand until creep fracture occurs,which is the damage and fracture mechanism of the alloy at the later stage of creep at high temperature.展开更多
Augite-based glass ceramics were synthesised using ZnO,FeO,and Fe_(2)O_(3)as additives,and the spinel formation,matrix structure,crystallisation thermodynamics,and physicochemical properties were investigated.The resu...Augite-based glass ceramics were synthesised using ZnO,FeO,and Fe_(2)O_(3)as additives,and the spinel formation,matrix structure,crystallisation thermodynamics,and physicochemical properties were investigated.The results showed that oxides resulted in numerous preliminary spinels in the glass matrix.FeO,ZnO,and Fe_(2)O_(3)influenced the formation of spinel,while FeO simplified the glass network.FeO and ZnO promoted bulk crystallisation of the parent glass.After adding oxides,the grains of augite phase were refined,and the relative quantities of augite crystal planes were also influenced.All samples displayed good mechanical properties and chemical stability.The 2wt%ZnO-doping sample displayed the maximum flexural strength(170.3 MPa).Chromium leaching amount values of all the samples were less than the national standard(1.5 mg/L),confirming the safety of the materials.In conclusion,an appropriate amount of zinc-containing raw material is beneficial for the preparation of augite-based glass ceramics.展开更多
The back-propagation neural （BPN） network was proposed to model the relationship between the parameters of the dieless draw- ing process and the microstrecmres of the QSi3-1 silicon bronze alloy. Combined with image...The back-propagation neural （BPN） network was proposed to model the relationship between the parameters of the dieless draw- ing process and the microstrecmres of the QSi3-1 silicon bronze alloy. Combined with image processing techniques, grain sizes and grain-boundary morphologies were respectively determined by the quantitative metallographic method and the flactal theory. The outcomes obtained show that the deformed microstructures exhibit typical fractal features, and the boundaries can be characterized quantitatively by ffactal dimensions. With the temperature of 600-800℃ and the drawing speed of 0.67-1.00 mm-s-1, either a lower temperature or a higher speed will cause a smaller grain size together with an elevated fractal dimension. The developed model can be capable for forecasting the microstructure evolution with a minimum error. The average relative errors between the predicted results and the experimental values of grain size and fractal dimension are 3.9% and 0.9%, respectively.展开更多
Grain shape of the hot deforming alloy is an important of material. The fractal theory was applied to analyze index to character the microstructure and performance the recrystallized microstructure of Ti-15-3 alloy af...Grain shape of the hot deforming alloy is an important of material. The fractal theory was applied to analyze index to character the microstructure and performance the recrystallized microstructure of Ti-15-3 alloy after hot deformation and solution treatment. The fractal dimensions of recrystallized grains were calculated by slit island method. The influence of processing parameters on fractal dimension and grain size was studied, It has been shown that the shapes of recrystallized grain boundaries are self-similar, and the fractal dimension varies from 1 to 2. With increasing deformation degree and strain rate or decreasing deformation temperature, the fractal dimension of grain boundaries increased and the grain size decreased. So the fractal dimension could characterize the grain shape and size. A neural network model was trained to predict the fractal dimension of recrystallized microstructure and the result is in excellent agreement with the experimental data.展开更多
Hot flow forming(HFF)is a promising forming technology to manufacture thin-walled cylindrical part with longitudinal inner ribs(CPLIRs)made of magnesium(Mg)alloys,which has wide applications in the aerospace field.How...Hot flow forming(HFF)is a promising forming technology to manufacture thin-walled cylindrical part with longitudinal inner ribs(CPLIRs)made of magnesium(Mg)alloys,which has wide applications in the aerospace field.However,due to the thermo-mechanical coupling effect and the existence of stiffened structure,complex microstructure evolution and uneven microstructure occur easily at the cylindrical wall(CW)and inner rib(IR)of Mg alloy thin-walled CPLIRs during the HFF.In this paper,a modified cellular automaton(CA)model of Mg alloy considering the effects of deformation conditions on material parameters was developed using the artificial neural network(ANN)method.It is found that the ANN-modified CA model exhibits better predictability for the microstructure of hot deformation than the conventional CA model.Furthermore,the microstructure evolution of ZK61 alloy CPLIRs during the HFF was analyzed by coupling the modified CA model and finite element analysis(FEA).The results show that compared with the microstructure at the same layer of the IR,more refined grains and less sufficient DRX resulted from larger strain and strain rate occur at that of the CW;various differences of strain and strain rate in the wall-thickness exist between the CW and IR,which leads to the inhomogeneity of microstructure rising firstly and declining from the inside layer to outside layer;the obtained Hall-Petch relationship between the measured microhardness and predicted grain sizes at the CW and the IR indicates the reliability of the coupled FEA-CA simulation results.展开更多
In modern industrial design trends featuring with integration,miniaturization,and versatility,there is a growing demand on the utilization of microstructural array devices.The measurement of such microstructural array...In modern industrial design trends featuring with integration,miniaturization,and versatility,there is a growing demand on the utilization of microstructural array devices.The measurement of such microstructural array components often encounters challenges due to the reduced scale and complex structures,either by contact or noncontact optical approaches.Among these microstructural arrays,there are still no optical measurement methods for micro corner-cube reflector arrays.To solve this problem,this study introduces a method for effectively eliminating coherent noise and achieving surface profile reconstruction in interference measurements of microstructural arrays.The proposed denoising method allows the calibration and inverse solving of system errors in the frequency domain by employing standard components with known surface types.This enables the effective compensation of the complex amplitude of non-sample coherent light within the interferometer optical path.The proposed surface reconstruction method enables the profile calculation within the situation that there is complex multi-reflection during the propagation of rays in microstructural arrays.Based on the measurement results,two novel metrics are defined to estimate diffraction errors at array junctions and comprehensive errors across multiple array elements,offering insights into other types of microstructure devices.This research not only addresses challenges of the coherent noise and multi-reflection,but also makes a breakthrough for quantitively optical interference measurement of microstructural array devices.展开更多
Refill friction stir spot welding process is difficultly optimized by accurate modeling because of the high-order functional relationship between welding parameters and joint strength.A database of the welding process...Refill friction stir spot welding process is difficultly optimized by accurate modeling because of the high-order functional relationship between welding parameters and joint strength.A database of the welding process was first established with 6061-T6 aluminum alloy and DP780 galvanized steel as base materials.This dataset was then optimized using a backpropagation neural network.Analyses and mining of the experimental data confirmed the multidimensional mapping relationship between welding parameters and joint strength.Subsequently,intelligent optimization of the welding process and prediction of joint strength were achieved.At the predicted welding parameter(plunging rotation speedω1=1733 r/min,refilling rotation speedω_(2)=1266 r/min,plunging depth p=1.9 mm,and welding speed v=0.5 mm/s),the tensile shear fracture load of the joint reached a maximum value of 10,172 N,while the experimental result was 9980 N,with an error of 1.92%.Furthermore,the correlation of welding parameters-microstructure-joint strength was established.展开更多
Two actual rocks drilled from a typical ultra-deep hydrocarbon reservoir in the Tarim Basin are selected to conduct in-situ stress-loading micro-focus CT scanning experiments.The gray images of rock microstructure at ...Two actual rocks drilled from a typical ultra-deep hydrocarbon reservoir in the Tarim Basin are selected to conduct in-situ stress-loading micro-focus CT scanning experiments.The gray images of rock microstructure at different stress loading stages are obtained.The U-Net fully convolutional neural network is utilized to achieve fine semantic segmentation of rock skeleton,pore space,and microfractures based on CT slice images of deep rocks.The three-dimensional digital rock models of deformed multiscale fractured-porous media at different stress loading stages are thereafter reconstructed,and the equivalent fracture-pore network models are finally extracted to explore the underlying mechanisms of gas-water two-phase flow at the pore-scale.Results indicate that,in the process of insitu stress loading,both the deep rocks have experienced three stages:linear elastic deformation,nonlinear plastic deformation,and shear failure.The micro-mechanical behavior greatly affects the dynamic deformation of rock microstructure and gas-water two-phase flow.In the linear elastic deformation stage,with the increase in in-situ stress,both the deep rocks are gradually compacted,leading to decreases in average pore radius,pore throat ratio,tortuosity,and water-phase relative permeability,while the coordination number nearly remains unchanged.In the plastic deformation stage,the synergistic influence of rock compaction and existence of micro-fractures typically exert a great effect on pore-throat topological properties and gas-water relative permeability.In the shear failure stage,due to the generation and propagation of micro-fractures inside the deep rock,the topological connectivity becomes better,fluid flow paths increase,and flow conductivity is promoted,thus leading to sharp increases in average pore radius and coordination number,rapid decreases in pore throat ratio and tortuosity,as well as remarkable improvement in relative permeability of gas phase and waterphase.展开更多
Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-por...Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-porosity and super-strong skeleton. The aluminum magnesium matrix composites reinforced with three-dimensional network structure were prepared using the infiltration technique by pressure assisting and vacuum driving. Light interfacial reactions have played a profitable role in most of the ceramic-metal systems. The metal matrix composites interpenetrated with the ceramic phase have a higher wear resistance than the metal matrix phase. The volume fraction of ceramic reinforcement has a significant effect on the abrasive wear, and the wear rate can be decreased with the increase of the volume fraction of reinforcement.展开更多
Laser cladding of powder mixture of TiN and SS304 is carried out on an SS304 substrate with the help of fibre laser.The experiments are performed on SS304,as per the Taguchi orthogonal array(L^(16))by different combin...Laser cladding of powder mixture of TiN and SS304 is carried out on an SS304 substrate with the help of fibre laser.The experiments are performed on SS304,as per the Taguchi orthogonal array(L^(16))by different combinations of controllable parameters(microhardness and clad thickness).The microhardness and clad thickness are recorded at all the experimental runs and studied using Taguchi S/N ratio and the optimum controllable parametric combination is obtained.However,an artificial neural network(ANN)identifies different sets of optimal combinations from Taguchi method but they both got almost the same clad thickness and hardness values.The micro-hardness of cladded layer is found to be6.22 times(HV_(0.5)752)the SS304 hardness(HV_(0.5)121).The presence of nitride ceramics results in a higher micro hardness.The cladded surface is free from cracks and pores.The average clad thickness is found to be around 0.6 mm.展开更多
The artificial neural network (ANN) and the pattern recognition were applied to study the correlation of enthalpies of fusion for divalent rare earth halides with their microstructural parameters,such as ionic radius ...The artificial neural network (ANN) and the pattern recognition were applied to study the correlation of enthalpies of fusion for divalent rare earth halides with their microstructural parameters,such as ionic radius and electronegativity. The model,represented by a back-propagation netal network, was trained with a 12 set of published data for divalent rare earth halides and then was used to predict the unknown ones. Also the criterion equations were ptesented to determine the enthalpies of fuSion for divalent rare earth halides using pattern recognition in mis work. The results from the model in ANN and criterion equations are in very good agreement with reference data.展开更多
基金financially supported by the National Key R&D Program of China (No. 2017YFB0703100)the National Natural Science Foundation of China (NSFC) under Grant Nos. 51822103, 51671068 and 51731009the Fundamental Research Funds for the Central Universities (No. HIT.BRETIV.201902)
文摘The improvement of mechanical properties must be achieved by designing and constructing more suitable microstructure,such as hierarchical microstructure.In order to significantly enhance the creep resistance of titanium matrix composites(TMCs),two-scale network microstructure was constructed including the first-scale network(<150μm)with micro-TiB whisker(TiBw)reinforcement and the second-scale network(<30μm)with nano-Ti5Si3 reinforcement by powder metallurgy and in-situ synthesis.The results showed that the creep rate of the composite was remarkably reduced by an order of magnitude compared with the Ti6Al4V alloy at 550℃,600℃,650℃ under the stresses between 100 MPa and 350 MPa.Moreover,the rupture time of the composite was increased by 20 times,compared with that of the Ti6Al4 Valloy at 550℃/300 MPa.The superior creep resistance could be attributed to the hierarchical microstructure.The micro-TiBw reinforcement in the first-scale network boundary contributed to creep resistance primarily by blocking grain boundary sliding,while the nano-Ti5Si3 particle in the second-scale network boundary mainly by hindering phase boundary sliding.In addition,the nano-Ti5Si3 particle was dissolved,and precipitated with smaller size than the primary Ti5Si3.This phenomenon was attributed to Si element diffusion under high temperature and external stress,which could further continuously enhance the creep resistance.Finally,the creep rate during steady-state stage was significantly decreased,which manifested superior creep resistance of the composite.
基金Project(51344004)supported by the National Natural Science Foundation of China
文摘The effects of the solid solution conditions on the microstructure and tensile properties of Al?Zn?Mg?Cu aluminum alloy were investigated by in-situ resistivity measurement, optical microscopy (OM), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and tensile test. A radial basis function artificial neural network (RBF-ANN) model was developed for the analysis and prediction of the electrical resistivity of the tested alloy during the solid solution process. The results show that the model is capable of predicting the electrical resistivity with remarkable success. The correlation coefficient between the predicted results and experimental data is 0.9958 and the relative error is 0.33%. The predicted data were adopted to construct a novel physical picture which was defined as “solution resistivity map”. As revealed by the map, the optimum domain for the solid solution of the tested alloy is in the temperature range of 465?475 °C and solution time range of 50?60 min. In this domain, the solution of second particles and the recrystallization phenomenon will reach equilibrium.
基金supported by a grant from the Institute for Guo Qiang.Tsinghua University(Grant No.2019GQG1012)Y.Z.acknowledges support from the National Natural Science Foundation of China(Grant Nos.11722217 and 11921002)+1 种基金the Tsinghua University Initiative Scientific Research Program(#2019Z08QCX10)the Henry Fok Education Foundation.
文摘Developments of soft network materials with rationally distributed wavy microstructures have enabled many promising applications in bio-integrated electronic devices,due to their abilities to reproduce precisely nonlinear mechanical properties of human tissues/organs.In practical applications,the soft network materials usually serve as the encapsulation layer and/or substrate of bio-integrated electronic devices,where deterministic holes can be utilized to accommodate hard chips,thereby increasing the filling ratio of the device system.Therefore,it is essential to understand how the hole-type imperfection affects the stretchability of soft network materialswith various geometric constructions.Thiswork presents a systematic investigation of the imperfection sensitivity of mechanical properties in soft network materials consisting of horseshoe microstructures,through combined computational and experimental studies.A factor of imperfection insensitivity of stretchability is introduced to quantify the influence of hole imperfections,as compared to the case of perfect soft network materials.Such factor is shown to have different dependences on the arc angle and normalized width of horseshoe microstructures for triangular network materials.The soft triangular and Kagome network materials,especially with the arc angle in the range of(30?,60?),are found to be much more imperfection insensitive than corresponding traditional lattice materials with straight microstructures.Differently,the soft honeycomb network materials are not as imperfection insensitive as traditional honeycomb lattice materials.
基金financially supported by the National Key Research and Development Plan of China (No.2016YFB0301300)the National Natural Science Foundation of China (Nos.51374025,51674027 and U1703131)the Beijing Municipal Natural Science Foundation (No.2152020)
文摘Microstructures of Cu-4.7Sn(%) alloys prepared by two-phase zone continuous casting(TZCC)technology contain large columnar grains and small grains.A compound grain structure,composed of a large columnar grain and at least one small grain within it,is observed and called as grain-covered grains(GCGs).Distribution of small grains,their numbers and sizes as well as numbers and sizes of columnar grains were characterized quantitatively by metallographic microscope.Back propagation(BP) artificial neural network was employed to build a model to predict microstructures produced by different processing parameters.Inputs of the model are five processing parameters,which are temperatures of melt,mold and cooling water,speed of TZCC,and cooling distance.Outputs of the model are nine microstructure quantities,which are numbers of small grains within columnar grains,at the boundaries of the columnar grains,or at the surface of the alloy,the maximum and the minimum numbers of small grains within a columnar grain,numbers of columnar grains with or without small grains,and sizes of small grains and columnar grains.The model yields precise prediction,which lays foundation for controlling microstructures of alloys prepared by TZCC.
基金Item Sponsored by National Natural Science Foundation of China (50474086)Program for New Century Excellent Talents in University (NECT-04-0278)
文摘An artificial neural network （ANN） model for predicting transformed mierostrueture in conventional rolling process and therrnomechanical controlled process （TMCP） is proposed. The model uses austenite grain size and retained strain, which can be calculated by using microstructure evolution models, together with a measured cooling rate and chemical compositions as inputs and the ferrite grain size and ferrite fraction as outputs. The predicted re- suits show that the model can predict the transformed microstructure which is in good agreement with the measured one, and it is better than the empirical equations. Also, the effect of the alloying elements on transformed products has been analyzed by using the model. The tendency is the same as that in the reported articles. The model can be used further for the optimization of processing parameters, mierostructure and properties in TMCP.
基金Guizhou Province Science and Technology Plan Project(QKHJC-ZK[2024]yiban604)Guizhou Province Science and Technology Plan Project(CXTD[2021]008)+4 种基金Bijie City Science and Technology Project(BKLH[2023]9)Technology Project of Bijie City(BKLH[2023]36)Natural Science Research Project of Guizhou Higher Education Institutions of China(QJJ[2023]047)Science and Technology Project of Guizhou Department of Transportation(2022-121-011)Sanmenxia City Science and Technology Bureau Science and Technology Research Project(2022002005)。
文摘The microstructure evolution and deformation mechanism of a DZ125 superalloy during high-temperature creep were studied by means of microstructure observation and creep-property tests.The results show that at the initial stage of high-temperature creep,two sets of dislocations with different Burgers vectors move and meet inγmatrix channels,and react to form a quadrilateral dislocation network.Andγ′phases with raft-like microstructure are generated after the formation of dislocation networks.As creep progresses,the quadrilateral dislocation network is gradually transformed into hexagonal and quadrilateral dislocation networks.During steady stage of creep,the superalloy undergoes deformation with the mechanism that a great number of dislocations slip and climb in the matrix across the raft-likeγ′phases.At the later stage of creep,the raft-likeγ′phases are sheared by dislocations at the breakage of dislocation networks,and then alternate slip occurs,which distorts and breaks the raft-likeγ′/γphases,resulting in the accumulation of micropores at the raft-likeγ′/γinterfaces and the formation of microcracks.As creep continues,the microcracks continue to expand until creep fracture occurs,which is the damage and fracture mechanism of the alloy at the later stage of creep at high temperature.
基金supported by the National Key R&D Program of China(No.2019YFC1905701)the National Natural Science Foundation of China(Nos.U1960201 and 52204336)the China Postdoctoral Science Foundation(No.2022M710359).
文摘Augite-based glass ceramics were synthesised using ZnO,FeO,and Fe_(2)O_(3)as additives,and the spinel formation,matrix structure,crystallisation thermodynamics,and physicochemical properties were investigated.The results showed that oxides resulted in numerous preliminary spinels in the glass matrix.FeO,ZnO,and Fe_(2)O_(3)influenced the formation of spinel,while FeO simplified the glass network.FeO and ZnO promoted bulk crystallisation of the parent glass.After adding oxides,the grains of augite phase were refined,and the relative quantities of augite crystal planes were also influenced.All samples displayed good mechanical properties and chemical stability.The 2wt%ZnO-doping sample displayed the maximum flexural strength(170.3 MPa).Chromium leaching amount values of all the samples were less than the national standard(1.5 mg/L),confirming the safety of the materials.In conclusion,an appropriate amount of zinc-containing raw material is beneficial for the preparation of augite-based glass ceramics.
基金supported by the National Basic Research Priorities Program of China (No.2006CB605200)the National Natu-ral Science Foundation of China (Nos.50674008 and 50634010)+1 种基金the Program for New Century Excellent Talents in Chinese Universities (No.NCET-06-0083)the Foundation of State Key Laboratory for Advanced Metals and Materials (No.2008Z-15)
文摘The back-propagation neural （BPN） network was proposed to model the relationship between the parameters of the dieless draw- ing process and the microstrecmres of the QSi3-1 silicon bronze alloy. Combined with image processing techniques, grain sizes and grain-boundary morphologies were respectively determined by the quantitative metallographic method and the flactal theory. The outcomes obtained show that the deformed microstructures exhibit typical fractal features, and the boundaries can be characterized quantitatively by ffactal dimensions. With the temperature of 600-800℃ and the drawing speed of 0.67-1.00 mm-s-1, either a lower temperature or a higher speed will cause a smaller grain size together with an elevated fractal dimension. The developed model can be capable for forecasting the microstructure evolution with a minimum error. The average relative errors between the predicted results and the experimental values of grain size and fractal dimension are 3.9% and 0.9%, respectively.
基金supported by the National Natural Science Foundation of China under grant No.50405020.
文摘Grain shape of the hot deforming alloy is an important of material. The fractal theory was applied to analyze index to character the microstructure and performance the recrystallized microstructure of Ti-15-3 alloy after hot deformation and solution treatment. The fractal dimensions of recrystallized grains were calculated by slit island method. The influence of processing parameters on fractal dimension and grain size was studied, It has been shown that the shapes of recrystallized grain boundaries are self-similar, and the fractal dimension varies from 1 to 2. With increasing deformation degree and strain rate or decreasing deformation temperature, the fractal dimension of grain boundaries increased and the grain size decreased. So the fractal dimension could characterize the grain shape and size. A neural network model was trained to predict the fractal dimension of recrystallized microstructure and the result is in excellent agreement with the experimental data.
基金supported by the National Nat-ural Science Foundation of China(Grant Nos.51775194 and 52090043).
文摘Hot flow forming(HFF)is a promising forming technology to manufacture thin-walled cylindrical part with longitudinal inner ribs(CPLIRs)made of magnesium(Mg)alloys,which has wide applications in the aerospace field.However,due to the thermo-mechanical coupling effect and the existence of stiffened structure,complex microstructure evolution and uneven microstructure occur easily at the cylindrical wall(CW)and inner rib(IR)of Mg alloy thin-walled CPLIRs during the HFF.In this paper,a modified cellular automaton(CA)model of Mg alloy considering the effects of deformation conditions on material parameters was developed using the artificial neural network(ANN)method.It is found that the ANN-modified CA model exhibits better predictability for the microstructure of hot deformation than the conventional CA model.Furthermore,the microstructure evolution of ZK61 alloy CPLIRs during the HFF was analyzed by coupling the modified CA model and finite element analysis(FEA).The results show that compared with the microstructure at the same layer of the IR,more refined grains and less sufficient DRX resulted from larger strain and strain rate occur at that of the CW;various differences of strain and strain rate in the wall-thickness exist between the CW and IR,which leads to the inhomogeneity of microstructure rising firstly and declining from the inside layer to outside layer;the obtained Hall-Petch relationship between the measured microhardness and predicted grain sizes at the CW and the IR indicates the reliability of the coupled FEA-CA simulation results.
基金Supported by National Natural Science Foundation of China(Grant Nos.52375414,52075100)Shanghai Science and Technology Committee Innovation Grant of China(Grant No.23ZR1404200).
文摘In modern industrial design trends featuring with integration,miniaturization,and versatility,there is a growing demand on the utilization of microstructural array devices.The measurement of such microstructural array components often encounters challenges due to the reduced scale and complex structures,either by contact or noncontact optical approaches.Among these microstructural arrays,there are still no optical measurement methods for micro corner-cube reflector arrays.To solve this problem,this study introduces a method for effectively eliminating coherent noise and achieving surface profile reconstruction in interference measurements of microstructural arrays.The proposed denoising method allows the calibration and inverse solving of system errors in the frequency domain by employing standard components with known surface types.This enables the effective compensation of the complex amplitude of non-sample coherent light within the interferometer optical path.The proposed surface reconstruction method enables the profile calculation within the situation that there is complex multi-reflection during the propagation of rays in microstructural arrays.Based on the measurement results,two novel metrics are defined to estimate diffraction errors at array junctions and comprehensive errors across multiple array elements,offering insights into other types of microstructure devices.This research not only addresses challenges of the coherent noise and multi-reflection,but also makes a breakthrough for quantitively optical interference measurement of microstructural array devices.
基金the financial supports provided by the National Key Research and Development Program of China(2023YFE0201500)the National Natural Science Foundation of China(52375315)+2 种基金the Key Talent Plan Project of Guangdong Province(2023TQ07C702)the Research and Development Program in Key Areas of Dongguan(20201200300122)the GDAS’Project of Science and Technology Development(2022GDASZH-2022010203).
文摘Refill friction stir spot welding process is difficultly optimized by accurate modeling because of the high-order functional relationship between welding parameters and joint strength.A database of the welding process was first established with 6061-T6 aluminum alloy and DP780 galvanized steel as base materials.This dataset was then optimized using a backpropagation neural network.Analyses and mining of the experimental data confirmed the multidimensional mapping relationship between welding parameters and joint strength.Subsequently,intelligent optimization of the welding process and prediction of joint strength were achieved.At the predicted welding parameter(plunging rotation speedω1=1733 r/min,refilling rotation speedω_(2)=1266 r/min,plunging depth p=1.9 mm,and welding speed v=0.5 mm/s),the tensile shear fracture load of the joint reached a maximum value of 10,172 N,while the experimental result was 9980 N,with an error of 1.92%.Furthermore,the correlation of welding parameters-microstructure-joint strength was established.
基金supported by the National Natural Science Foundation of China(No.52174043)the Beijing Natural Science Foundation(No.3242019)+1 种基金the CNPC Innovation Foundation(No.2022DQ02-0208)the State Key Laboratory of Deep Oil and Gas(No.SKLD0G2024-KFZD-06).
文摘Two actual rocks drilled from a typical ultra-deep hydrocarbon reservoir in the Tarim Basin are selected to conduct in-situ stress-loading micro-focus CT scanning experiments.The gray images of rock microstructure at different stress loading stages are obtained.The U-Net fully convolutional neural network is utilized to achieve fine semantic segmentation of rock skeleton,pore space,and microfractures based on CT slice images of deep rocks.The three-dimensional digital rock models of deformed multiscale fractured-porous media at different stress loading stages are thereafter reconstructed,and the equivalent fracture-pore network models are finally extracted to explore the underlying mechanisms of gas-water two-phase flow at the pore-scale.Results indicate that,in the process of insitu stress loading,both the deep rocks have experienced three stages:linear elastic deformation,nonlinear plastic deformation,and shear failure.The micro-mechanical behavior greatly affects the dynamic deformation of rock microstructure and gas-water two-phase flow.In the linear elastic deformation stage,with the increase in in-situ stress,both the deep rocks are gradually compacted,leading to decreases in average pore radius,pore throat ratio,tortuosity,and water-phase relative permeability,while the coordination number nearly remains unchanged.In the plastic deformation stage,the synergistic influence of rock compaction and existence of micro-fractures typically exert a great effect on pore-throat topological properties and gas-water relative permeability.In the shear failure stage,due to the generation and propagation of micro-fractures inside the deep rock,the topological connectivity becomes better,fluid flow paths increase,and flow conductivity is promoted,thus leading to sharp increases in average pore radius and coordination number,rapid decreases in pore throat ratio and tortuosity,as well as remarkable improvement in relative permeability of gas phase and waterphase.
基金This work was financially supported by the Natural Science Foundation of Shandong Province, China (Y2006F03).
文摘Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-porosity and super-strong skeleton. The aluminum magnesium matrix composites reinforced with three-dimensional network structure were prepared using the infiltration technique by pressure assisting and vacuum driving. Light interfacial reactions have played a profitable role in most of the ceramic-metal systems. The metal matrix composites interpenetrated with the ceramic phase have a higher wear resistance than the metal matrix phase. The volume fraction of ceramic reinforcement has a significant effect on the abrasive wear, and the wear rate can be decreased with the increase of the volume fraction of reinforcement.
文摘Laser cladding of powder mixture of TiN and SS304 is carried out on an SS304 substrate with the help of fibre laser.The experiments are performed on SS304,as per the Taguchi orthogonal array(L^(16))by different combinations of controllable parameters(microhardness and clad thickness).The microhardness and clad thickness are recorded at all the experimental runs and studied using Taguchi S/N ratio and the optimum controllable parametric combination is obtained.However,an artificial neural network(ANN)identifies different sets of optimal combinations from Taguchi method but they both got almost the same clad thickness and hardness values.The micro-hardness of cladded layer is found to be6.22 times(HV_(0.5)752)the SS304 hardness(HV_(0.5)121).The presence of nitride ceramics results in a higher micro hardness.The cladded surface is free from cracks and pores.The average clad thickness is found to be around 0.6 mm.
文摘The artificial neural network (ANN) and the pattern recognition were applied to study the correlation of enthalpies of fusion for divalent rare earth halides with their microstructural parameters,such as ionic radius and electronegativity. The model,represented by a back-propagation netal network, was trained with a 12 set of published data for divalent rare earth halides and then was used to predict the unknown ones. Also the criterion equations were ptesented to determine the enthalpies of fuSion for divalent rare earth halides using pattern recognition in mis work. The results from the model in ANN and criterion equations are in very good agreement with reference data.
