The evolution of misfit dislocation network at γ/γ＇ phase interfaces and the stress distribution characteristics of Ni-based single-crystal superalloys under different temperatures of 0, 100 and 300 K are studied b...The evolution of misfit dislocation network at γ/γ＇ phase interfaces and the stress distribution characteristics of Ni-based single-crystal superalloys under different temperatures of 0, 100 and 300 K are studied by molecular dynamics （MD） simulation. It was found that a closed three-dimensional misfit dislocation network appears on the γ/γ＇ phase interfaces, and the shape of the dislocation network is independent of the lattice mismatch. Under the influence of the temperature, the dislocation network gradually becomes irregular, a/2 [110] dislocations in the γ matrix phase emit and partly cut into the γ＇ phase with the increase in temperature. The dislocation evolution is related to the local stress field, a peak stress occurs at γ/γ＇ phase interface, and with the increase in temperature and relaxation times, the stress in the γ phase gradually increases, the number of dislocations in the γ phase increases and cuts into γ＇ phase from the interfaces where dislocation network is damaged. The results provide important information for understanding the temperature dependence of the dislocation evolution and mechanical properties of Ni-based single-crystal superalloys.展开更多
Simulations are conducted using five new artificial neural networks developed herein to demonstrate and investigate the behavior of rock material under polyaxial loading. The effects of the intermediate principal stre...Simulations are conducted using five new artificial neural networks developed herein to demonstrate and investigate the behavior of rock material under polyaxial loading. The effects of the intermediate principal stress on the intact rock strength are investigated and compared with laboratory results from the literature. To normalize differences in laboratory testing conditions, the stress state is used as the objective parameter in the artificial neural network model predictions. The variations of major principal stress of rock material with intermediate principal stress, minor principal stress and stress state are investigated. The artificial neural network simulations show that for the rock types examined, none were independent of intermediate principal stress effects. In addition, the results of the artificial neural network models, in general agreement with observations made by others, show （a） a general trend of strength increasing and reaching a peak at some intermediate stress state factor, followed by a decline in strength for most rock types; （b） a post-peak strength behavior dependent on the minor principal stress, with respect to rock type; （c） sensitivity to the stress state, and to the interaction between the stress state and uniaxial compressive strength of the test data by the artificial neural networks models （two-way analysis of variance; 95% confidence interval）. Artificial neural network modeling, a self-learning approach to polyaxial stress simulation, can thus complement the commonly observed difficult task of conducting true triaxial laboratory tests, and/or other methods that attempt to improve two-dimensional （2D） failure criteria by incorporating intermediate principal stress effects.展开更多
Alumina dispersion strengthened copper composite （nano-Al2O3/Cu composite） was recently emerged as a kind of potentially viable and attractive engineering material for applications requiring high strength, high ther...Alumina dispersion strengthened copper composite （nano-Al2O3/Cu composite） was recently emerged as a kind of potentially viable and attractive engineering material for applications requiring high strength, high thermal and electrical conductivities and resistance to softening at elevated temperatures. The nano-Al2O3/Cu composite was produced by internal oxidation. The microstructures of the composite were analyzed by the TEM and its hot deformation behavior was investigated by means of continuous compression tests performed on a Gleeble 1500 thermo-simulator. Making use of the modified algorithm-Levenberg-Marquardt （L-M） algorithm BP neural network, a model for predicting the flow stresses during hot deformation was set up on the base of the experimental data. Results show that the microstructures of the composite are characterized by uniform distribution of nano-Al2O3 particles in Cu-matrix. The sliding of dislocations is the main deformation mechanism. The dynamic recovery is the main softening mode with the flow stress decreasing gently from 500℃ to 850 ~C. The recrystallization of Cu-matrix can be retarded late into as high as 850 ℃, when it happens only partially. The well-trained BP neural network model can accurately describe the influence of the temperature, strain rate, and true strain on the flow stresses, therefore, it can precisely predict the flow stresses of the composite under given deforming conditions and provide a new way to optimize hot deforming process parameters.展开更多
On the basis of the data obtained on Gleeble 1500 Thermal Simulator, the predicting models for the relation between stable flow stress during high temperature plastic deformation and deformation strain, strain rate an...On the basis of the data obtained on Gleeble 1500 Thermal Simulator, the predicting models for the relation between stable flow stress during high temperature plastic deformation and deformation strain, strain rate and temperature for 1420 Al Li alloy have been developed with BP artificial neural networks method. The results show that the model on basis of BPNN is practical and it reflects the actual feature of the deforming process. It states that the difference between the actual value and the output of the model is in order of 5%. [展开更多
Hot compression experiments were conducted on Ti 15 3 alloy specimens using Gleeble 1500 Thermal Simulator.These tests were focused to obtain the flow stress data under various conditions of strain,strain rate and tem...Hot compression experiments were conducted on Ti 15 3 alloy specimens using Gleeble 1500 Thermal Simulator.These tests were focused to obtain the flow stress data under various conditions of strain,strain rate and temperature. On the basis of these data, the predicting model for the nonlinear relation between flow stress and deformation strain,strain rate and temperature for Ti 15 3 alloy was developed with a back propagation artificial neural network method. Results show that the neural network can reproduce the flow stress in the sampled data and predict the nonsampled data well. Thus the neural network method has been verified to be used to tackle hot deformation problems of Ti 15 3 alloy. [展开更多
For the great significance of the prediction of control parameters selected for hot-rolling and the evaluation of hot-rolling quality for the analysis of prod uction problems and production management, the selection o...For the great significance of the prediction of control parameters selected for hot-rolling and the evaluation of hot-rolling quality for the analysis of prod uction problems and production management, the selection of hot-rolling control parameters was studied for microalloy steel by following the neural network principle. An experimental scheme was first worked out for acquisition of sample data, in which a gleeble-1500 thermal simolator was used to obtain rolling temperature, strain, stain rate, and stress-strain curves. And consequently the aust enite grain sizes was obtained through microscopic observation. The experimental data was then processed through regression. By using the training network of BP algorithm, the mapping relationship between the hotrooling control parameters (rolling temperature, stain, and strain rate) and the microstructural paramete rs (austenite grain in size and flow stress) of microalloy steel was function appro ached for the establishment of a neural network-based model of the austeuite grain size and flow stress of microalloy steel. From the results of estimation made with the neural network based model, the hot-rolling control parameters can be effectively predicted.展开更多
Mechanochromic hydrogels, a new class of stimuli-responsive soft materials, have potential applications in a number of fields such as damage reporting and stress/strain sensing. We prepared a novel mechanochromic hydr...Mechanochromic hydrogels, a new class of stimuli-responsive soft materials, have potential applications in a number of fields such as damage reporting and stress/strain sensing. We prepared a novel mechanochromic hydrogel using a strategy that has been developed to prepare dual-network(DN) hydrogels. A hydrophobic rhodamine derivative(Rh mechanophore) was covalently incorporated into a first network as a cross-linker. This first network embedded with Rh mechanophore within the DN hydrogel was pre-stretched. This guaranteed that the stress could be transferred extensively to the Rh-crosslinked first network once the hydrogel was under an applied force. Interestingly, we found that the threshold stress required to activate the mechanochromism of the hydrogel was less than 200 kPa, and much less than those in previous reports. Moreover, because of the excellent sensitivity of the hydrogel to stress, the DN hydrogel exhibited reversible freezing-induced mechanochromism. Benefiting from the sensitivity of Rh mechanophore to both p H and force, the DN hydrogel showed p H-regulated mechanochromic behavior. Our experimental results indicate that the preparation strategy we used introduces sensitive mechanochromism into the hydrogel and preserves the advantageous mechanical properties of the DN hydrogel. These results will be beneficial to the design and preparation of mechanochromic hydrogels with high stress sensitivity, and foster their practical applications in a number of fields such as damage reporting and stress/strain sensing.展开更多
Subjective: This study aimed to investigate the therapeutic mechanisms of 7-hydroxyflavone (7-HF) in treating myocardial ischemia/reperfusion injury (MI/RI) via network pharmacology, molecular docking, target validati...Subjective: This study aimed to investigate the therapeutic mechanisms of 7-hydroxyflavone (7-HF) in treating myocardial ischemia/reperfusion injury (MI/RI) via network pharmacology, molecular docking, target validation, and experiments at the animal level. Methods: Firstly, the genes of 7-HF were acquired from PharmMapper, TCMSP, and SwissTargetPrediction. At the same time, MI/RI-related genes were obtained from OMIM, GeneCards, and TTD online platforms. Subsequently, string platform and Cytoscape 3.9.2 were used to construct protein-protein interaction network diagrams and 7-HF-targets-signaling pathways-MI/RI network. Then, the Metascape platform was used to conduct functional enrichment analyses. Next, AutoDock Vina and Pymol were used to perform molecular docking. The hub targets were validated in the GSE66360. Lastly, SOD, MDA, transmission electron microscope, quantitative real-time PCR, and western blot were used to validate in MI/RI rats. Results: 139 genes of 7-HF, 4832 genes of MI/RI were obtained. The 47 interact genes between 7-HF and MI/RI targets for MI/RI were likely to act through multiple pathways. And NQO1 was a critical target in the above process. In an animal experiment, 7-HF could relieve the injured interfibrillar mitochondria and myocardial fibers, decrease the expression of MDA and SOD, and increase the expression of Nrf2, NQO1 and HO-1 in the mRNA and protein level in the MI/RI rats. Conclusion: This study preliminarily demonstrated that 7-HF could provide cardioprotection by inhibiting the oxidative stress and up-regulating Nrf2/NQO1/HO-1 signaling pathway based on network pharmacology, molecular docking, target validation, and animal experiments.展开更多
In this study a neural network approach is proposed to realize an automatic numerical prediction of the interfacial friction factor and the flow stress of materials. Decrease in the inner diameter and reduction in the...In this study a neural network approach is proposed to realize an automatic numerical prediction of the interfacial friction factor and the flow stress of materials. Decrease in the inner diameter and reduction in the height of the ring are taken as input展开更多
The hot deformation behavior of TI (18W-4Cr-1V) high-speed steel was investigated by means of continuous compression tests performed on Gleeble 1500 thermomechan- ical simulator in a wide range of tempemtures (950℃...The hot deformation behavior of TI (18W-4Cr-1V) high-speed steel was investigated by means of continuous compression tests performed on Gleeble 1500 thermomechan- ical simulator in a wide range of tempemtures (950℃-1150℃) with strain rotes of 0.001s-1-10s-1 and true strains of 0-0. 7. The flow stress at the above hot defor- mation conditions is predicted by using BP artificial neural network. The architecture of network includes there are three input parameters:strain rate,temperature T and true strain , and just one output parameter, the flow stress ,2 hidden layers are adopted, the first hidden layer includes 9 neurons and second 10 negroes. It has been verified that BP artificial neural network with 3-9-10-1 architecture can predict flow stress of high-speed steel during hot deformation very well. Compared with the prediction method of flow stress by using Zaped-Holloman parumeter and hyperbolic sine stress function, the prediction method by using BP artificial neurul network has higher efficiency and accuracy.展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.11102139 and 11472195)the Natural Science Foundation of Hubei Province of China(No.2014CFB713)
文摘The evolution of misfit dislocation network at γ/γ＇ phase interfaces and the stress distribution characteristics of Ni-based single-crystal superalloys under different temperatures of 0, 100 and 300 K are studied by molecular dynamics （MD） simulation. It was found that a closed three-dimensional misfit dislocation network appears on the γ/γ＇ phase interfaces, and the shape of the dislocation network is independent of the lattice mismatch. Under the influence of the temperature, the dislocation network gradually becomes irregular, a/2 [110] dislocations in the γ matrix phase emit and partly cut into the γ＇ phase with the increase in temperature. The dislocation evolution is related to the local stress field, a peak stress occurs at γ/γ＇ phase interface, and with the increase in temperature and relaxation times, the stress in the γ phase gradually increases, the number of dislocations in the γ phase increases and cuts into γ＇ phase from the interfaces where dislocation network is damaged. The results provide important information for understanding the temperature dependence of the dislocation evolution and mechanical properties of Ni-based single-crystal superalloys.
文摘Simulations are conducted using five new artificial neural networks developed herein to demonstrate and investigate the behavior of rock material under polyaxial loading. The effects of the intermediate principal stress on the intact rock strength are investigated and compared with laboratory results from the literature. To normalize differences in laboratory testing conditions, the stress state is used as the objective parameter in the artificial neural network model predictions. The variations of major principal stress of rock material with intermediate principal stress, minor principal stress and stress state are investigated. The artificial neural network simulations show that for the rock types examined, none were independent of intermediate principal stress effects. In addition, the results of the artificial neural network models, in general agreement with observations made by others, show （a） a general trend of strength increasing and reaching a peak at some intermediate stress state factor, followed by a decline in strength for most rock types; （b） a post-peak strength behavior dependent on the minor principal stress, with respect to rock type; （c） sensitivity to the stress state, and to the interaction between the stress state and uniaxial compressive strength of the test data by the artificial neural networks models （two-way analysis of variance; 95% confidence interval）. Artificial neural network modeling, a self-learning approach to polyaxial stress simulation, can thus complement the commonly observed difficult task of conducting true triaxial laboratory tests, and/or other methods that attempt to improve two-dimensional （2D） failure criteria by incorporating intermediate principal stress effects.
基金Henan Innovation Project for University Prominent Research Talents (2007KYCX008)Henan Major Science and Technol-ogy Project (0523021500)+1 种基金Henan University of Science and Technology Major Pre-research Foundation (2005ZD003)Henan University of Science and Technology Personnel Scientific Research Foundation
文摘Alumina dispersion strengthened copper composite （nano-Al2O3/Cu composite） was recently emerged as a kind of potentially viable and attractive engineering material for applications requiring high strength, high thermal and electrical conductivities and resistance to softening at elevated temperatures. The nano-Al2O3/Cu composite was produced by internal oxidation. The microstructures of the composite were analyzed by the TEM and its hot deformation behavior was investigated by means of continuous compression tests performed on a Gleeble 1500 thermo-simulator. Making use of the modified algorithm-Levenberg-Marquardt （L-M） algorithm BP neural network, a model for predicting the flow stresses during hot deformation was set up on the base of the experimental data. Results show that the microstructures of the composite are characterized by uniform distribution of nano-Al2O3 particles in Cu-matrix. The sliding of dislocations is the main deformation mechanism. The dynamic recovery is the main softening mode with the flow stress decreasing gently from 500℃ to 850 ~C. The recrystallization of Cu-matrix can be retarded late into as high as 850 ℃, when it happens only partially. The well-trained BP neural network model can accurately describe the influence of the temperature, strain rate, and true strain on the flow stresses, therefore, it can precisely predict the flow stresses of the composite under given deforming conditions and provide a new way to optimize hot deforming process parameters.
文摘On the basis of the data obtained on Gleeble 1500 Thermal Simulator, the predicting models for the relation between stable flow stress during high temperature plastic deformation and deformation strain, strain rate and temperature for 1420 Al Li alloy have been developed with BP artificial neural networks method. The results show that the model on basis of BPNN is practical and it reflects the actual feature of the deforming process. It states that the difference between the actual value and the output of the model is in order of 5%. [
文摘Hot compression experiments were conducted on Ti 15 3 alloy specimens using Gleeble 1500 Thermal Simulator.These tests were focused to obtain the flow stress data under various conditions of strain,strain rate and temperature. On the basis of these data, the predicting model for the nonlinear relation between flow stress and deformation strain,strain rate and temperature for Ti 15 3 alloy was developed with a back propagation artificial neural network method. Results show that the neural network can reproduce the flow stress in the sampled data and predict the nonsampled data well. Thus the neural network method has been verified to be used to tackle hot deformation problems of Ti 15 3 alloy. [
文摘For the great significance of the prediction of control parameters selected for hot-rolling and the evaluation of hot-rolling quality for the analysis of prod uction problems and production management, the selection of hot-rolling control parameters was studied for microalloy steel by following the neural network principle. An experimental scheme was first worked out for acquisition of sample data, in which a gleeble-1500 thermal simolator was used to obtain rolling temperature, strain, stain rate, and stress-strain curves. And consequently the aust enite grain sizes was obtained through microscopic observation. The experimental data was then processed through regression. By using the training network of BP algorithm, the mapping relationship between the hotrooling control parameters (rolling temperature, stain, and strain rate) and the microstructural paramete rs (austenite grain in size and flow stress) of microalloy steel was function appro ached for the establishment of a neural network-based model of the austeuite grain size and flow stress of microalloy steel. From the results of estimation made with the neural network based model, the hot-rolling control parameters can be effectively predicted.
基金financially supported by the National Natural Science Foundation of China (No. 51273189)the National Science and Technology Major Project of the Ministry of Science and Technology of China (No. 2016ZX05016)the National Science and Technology Major Project of the Ministry of Science and Technology of China (No. 2016ZX05046)
文摘Mechanochromic hydrogels, a new class of stimuli-responsive soft materials, have potential applications in a number of fields such as damage reporting and stress/strain sensing. We prepared a novel mechanochromic hydrogel using a strategy that has been developed to prepare dual-network(DN) hydrogels. A hydrophobic rhodamine derivative(Rh mechanophore) was covalently incorporated into a first network as a cross-linker. This first network embedded with Rh mechanophore within the DN hydrogel was pre-stretched. This guaranteed that the stress could be transferred extensively to the Rh-crosslinked first network once the hydrogel was under an applied force. Interestingly, we found that the threshold stress required to activate the mechanochromism of the hydrogel was less than 200 kPa, and much less than those in previous reports. Moreover, because of the excellent sensitivity of the hydrogel to stress, the DN hydrogel exhibited reversible freezing-induced mechanochromism. Benefiting from the sensitivity of Rh mechanophore to both p H and force, the DN hydrogel showed p H-regulated mechanochromic behavior. Our experimental results indicate that the preparation strategy we used introduces sensitive mechanochromism into the hydrogel and preserves the advantageous mechanical properties of the DN hydrogel. These results will be beneficial to the design and preparation of mechanochromic hydrogels with high stress sensitivity, and foster their practical applications in a number of fields such as damage reporting and stress/strain sensing.
文摘Subjective: This study aimed to investigate the therapeutic mechanisms of 7-hydroxyflavone (7-HF) in treating myocardial ischemia/reperfusion injury (MI/RI) via network pharmacology, molecular docking, target validation, and experiments at the animal level. Methods: Firstly, the genes of 7-HF were acquired from PharmMapper, TCMSP, and SwissTargetPrediction. At the same time, MI/RI-related genes were obtained from OMIM, GeneCards, and TTD online platforms. Subsequently, string platform and Cytoscape 3.9.2 were used to construct protein-protein interaction network diagrams and 7-HF-targets-signaling pathways-MI/RI network. Then, the Metascape platform was used to conduct functional enrichment analyses. Next, AutoDock Vina and Pymol were used to perform molecular docking. The hub targets were validated in the GSE66360. Lastly, SOD, MDA, transmission electron microscope, quantitative real-time PCR, and western blot were used to validate in MI/RI rats. Results: 139 genes of 7-HF, 4832 genes of MI/RI were obtained. The 47 interact genes between 7-HF and MI/RI targets for MI/RI were likely to act through multiple pathways. And NQO1 was a critical target in the above process. In an animal experiment, 7-HF could relieve the injured interfibrillar mitochondria and myocardial fibers, decrease the expression of MDA and SOD, and increase the expression of Nrf2, NQO1 and HO-1 in the mRNA and protein level in the MI/RI rats. Conclusion: This study preliminarily demonstrated that 7-HF could provide cardioprotection by inhibiting the oxidative stress and up-regulating Nrf2/NQO1/HO-1 signaling pathway based on network pharmacology, molecular docking, target validation, and animal experiments.
文摘In this study a neural network approach is proposed to realize an automatic numerical prediction of the interfacial friction factor and the flow stress of materials. Decrease in the inner diameter and reduction in the height of the ring are taken as input
文摘The hot deformation behavior of TI (18W-4Cr-1V) high-speed steel was investigated by means of continuous compression tests performed on Gleeble 1500 thermomechan- ical simulator in a wide range of tempemtures (950℃-1150℃) with strain rotes of 0.001s-1-10s-1 and true strains of 0-0. 7. The flow stress at the above hot defor- mation conditions is predicted by using BP artificial neural network. The architecture of network includes there are three input parameters:strain rate,temperature T and true strain , and just one output parameter, the flow stress ,2 hidden layers are adopted, the first hidden layer includes 9 neurons and second 10 negroes. It has been verified that BP artificial neural network with 3-9-10-1 architecture can predict flow stress of high-speed steel during hot deformation very well. Compared with the prediction method of flow stress by using Zaped-Holloman parumeter and hyperbolic sine stress function, the prediction method by using BP artificial neurul network has higher efficiency and accuracy.
