This article, in order to unearth the deformation mechanism of particle reinforced metal matrix composites, establishes a finite element model （FEM） based on the actual microstructures. It finds out and analyzes the...This article, in order to unearth the deformation mechanism of particle reinforced metal matrix composites, establishes a finite element model （FEM） based on the actual microstructures. It finds out and analyzes the distribution of von Mises effective stress, strain and the maximum principal stress in the matrix and particles. Moreover, the overall stress and strain in the matrix and composites are calculated. By comparison, a tiny discrepancy exists between the experimental results and the simulation with respect to flow stresses. The effects of deformation parameters, such as temperature and strain rate, on the strengthening mechanism are explored and proved to be weak.展开更多
Traditional methods for measuring single-cell mechanical characteristics face several challenges,including lengthy measurement times,low throughput,and a requirement for advanced technical skills.To overcome these cha...Traditional methods for measuring single-cell mechanical characteristics face several challenges,including lengthy measurement times,low throughput,and a requirement for advanced technical skills.To overcome these challenges,a novel machine learning(ML)approach is implemented based on the convolutional neural networks(CNNs),aiming at predicting cells'elastic modulus and constitutive equations from their deformations while passing through micro-constriction channels.In the present study,the computational fluid dynamics technology is used to generate a dataset within the range of the cell elastic modulus,incorporating three widely-used constitutive models that characterize the cellular mechanical behavior,i.e.,the Mooney-Rivlin(M-R),Neo-Hookean(N-H),and Kelvin-Voigt(K-V)models.Utilizing this dataset,a multi-input convolutional neural network(MI-CNN)algorithm is developed by incorporating cellular deformation data as well as the time and positional information.This approach accurately predicts the cell elastic modulus,with a coefficient of determination R^(2)of 0.999,a root mean square error of 0.218,and a mean absolute percentage error of 1.089%.The model consistently achieves high-precision predictions of the cellular elastic modulus with a maximum R^(2)of 0.99,even when the stochastic noise is added to the simulated data.One significant feature of the present model is that it has the ability to effectively classify the three types of constitutive equations we applied.The model accurately and reliably predicts single-cell mechanical properties,showcasing a robust ability to generalize.We demonstrate that incorporating deformation features at multiple time points can enhance the algorithm's accuracy and generalization.This algorithm presents a possibility for high-throughput,highly automated,real-time,and precise characterization of single-cell mechanical properties.展开更多
Detailed morphological data of vascular smooth muscle cells (VSMC) of coronary arteries were limited. The present study was to quantify dimensions and orientation of swine coronary VSMC and to develop a micro-structur...Detailed morphological data of vascular smooth muscle cells (VSMC) of coronary arteries were limited. The present study was to quantify dimensions and orientation of swine coronary VSMC and to develop a micro-structural constitutive model of active media. It was found that geometrical parameters of VSMC (length, width, spatial aspect ratio, and orientation) follow normal distributions, and VSMCs orientate towards the circumferential direction of vessels with oblique and symmetrical angles. A micro-structural model of media layer was developed to?accurately predict biaxial active responses of coronary arterial media, based on experimental measurements. The present morphological data base and micro-structural model lead to a better understanding of biomechanics of muscular vessels.展开更多
The coupling effects of ultrasonic excitation and high-strain-rate deformation are the core factors for weld formation during ultrasonic welding.However,interfacial deformation behavior still shrouds in uncer-tainty b...The coupling effects of ultrasonic excitation and high-strain-rate deformation are the core factors for weld formation during ultrasonic welding.However,interfacial deformation behavior still shrouds in uncer-tainty because of the contradictory features between mutual dislocation retardation caused by severely frictional deformation and ultrasonic-accelerated dislocation motion.[101]and[111]-oriented Cu single crystals which tended to form geometrically necessary boundaries(GNBs)were selected as the welding substrates to trace the uniquely acoustoplastic effects in the interfacial region under the ultrasonically excited high-strain-rate deformation.It was indicated that for a low energy input,micro-welds localized at the specific interface region,and equiaxed dislocation cells substituting for GNBs dominated in the ini-tial single crystal rotation region.As the welding energy increased,continuous shear deformation drove the dynamic recrystallization region covered by equiaxed grains to spread progressively.Limited discrete dislocations inside the recrystallized grains and nascent dislocation cells at the grain boundaries were ob-served in[101]and[111]joints simultaneously,suggesting that the ultrasonic excitation promoted motion of intragranular dislocation and pile-up along the sub-grain boundaries.The interfacial morphology be-fore and after expansion of recrystallization region all exhibited the weakening of orientation constraint on dislocation motion,which was also confirmed by the similar micro-hardness in joint interface.The first-principle calculation and applied strain-rate analysis further revealed that ultrasonic excitation en-hanced dislocation slipping,and enabled dislocation motion to accommodate severe plastic deformation at a high-strain-rate.展开更多
基金Aeronautical Science Foundation of China （03H53048）
文摘This article, in order to unearth the deformation mechanism of particle reinforced metal matrix composites, establishes a finite element model （FEM） based on the actual microstructures. It finds out and analyzes the distribution of von Mises effective stress, strain and the maximum principal stress in the matrix and particles. Moreover, the overall stress and strain in the matrix and composites are calculated. By comparison, a tiny discrepancy exists between the experimental results and the simulation with respect to flow stresses. The effects of deformation parameters, such as temperature and strain rate, on the strengthening mechanism are explored and proved to be weak.
基金Project supported by the National Natural Science Foundation of China(Nos.12332016,12172209,and 12202258)the Shanghai Gaofeng Project for University Academic Program Development。
文摘Traditional methods for measuring single-cell mechanical characteristics face several challenges,including lengthy measurement times,low throughput,and a requirement for advanced technical skills.To overcome these challenges,a novel machine learning(ML)approach is implemented based on the convolutional neural networks(CNNs),aiming at predicting cells'elastic modulus and constitutive equations from their deformations while passing through micro-constriction channels.In the present study,the computational fluid dynamics technology is used to generate a dataset within the range of the cell elastic modulus,incorporating three widely-used constitutive models that characterize the cellular mechanical behavior,i.e.,the Mooney-Rivlin(M-R),Neo-Hookean(N-H),and Kelvin-Voigt(K-V)models.Utilizing this dataset,a multi-input convolutional neural network(MI-CNN)algorithm is developed by incorporating cellular deformation data as well as the time and positional information.This approach accurately predicts the cell elastic modulus,with a coefficient of determination R^(2)of 0.999,a root mean square error of 0.218,and a mean absolute percentage error of 1.089%.The model consistently achieves high-precision predictions of the cellular elastic modulus with a maximum R^(2)of 0.99,even when the stochastic noise is added to the simulated data.One significant feature of the present model is that it has the ability to effectively classify the three types of constitutive equations we applied.The model accurately and reliably predicts single-cell mechanical properties,showcasing a robust ability to generalize.We demonstrate that incorporating deformation features at multiple time points can enhance the algorithm's accuracy and generalization.This algorithm presents a possibility for high-throughput,highly automated,real-time,and precise characterization of single-cell mechanical properties.
文摘Detailed morphological data of vascular smooth muscle cells (VSMC) of coronary arteries were limited. The present study was to quantify dimensions and orientation of swine coronary VSMC and to develop a micro-structural constitutive model of active media. It was found that geometrical parameters of VSMC (length, width, spatial aspect ratio, and orientation) follow normal distributions, and VSMCs orientate towards the circumferential direction of vessels with oblique and symmetrical angles. A micro-structural model of media layer was developed to?accurately predict biaxial active responses of coronary arterial media, based on experimental measurements. The present morphological data base and micro-structural model lead to a better understanding of biomechanics of muscular vessels.
基金supported by the National Nat-ural Science Foundation of China(No.52175310)A part of the work was also supported by the National Science and Technology Major Project(No.2017-VI-0009-0080)+1 种基金the Guang-dong Province Key Research and Development Program(No.2019B010935001)and the Shenzhen Science and Technology Plan(No.GXWD20201230155427003-20200821172456002).
文摘The coupling effects of ultrasonic excitation and high-strain-rate deformation are the core factors for weld formation during ultrasonic welding.However,interfacial deformation behavior still shrouds in uncer-tainty because of the contradictory features between mutual dislocation retardation caused by severely frictional deformation and ultrasonic-accelerated dislocation motion.[101]and[111]-oriented Cu single crystals which tended to form geometrically necessary boundaries(GNBs)were selected as the welding substrates to trace the uniquely acoustoplastic effects in the interfacial region under the ultrasonically excited high-strain-rate deformation.It was indicated that for a low energy input,micro-welds localized at the specific interface region,and equiaxed dislocation cells substituting for GNBs dominated in the ini-tial single crystal rotation region.As the welding energy increased,continuous shear deformation drove the dynamic recrystallization region covered by equiaxed grains to spread progressively.Limited discrete dislocations inside the recrystallized grains and nascent dislocation cells at the grain boundaries were ob-served in[101]and[111]joints simultaneously,suggesting that the ultrasonic excitation promoted motion of intragranular dislocation and pile-up along the sub-grain boundaries.The interfacial morphology be-fore and after expansion of recrystallization region all exhibited the weakening of orientation constraint on dislocation motion,which was also confirmed by the similar micro-hardness in joint interface.The first-principle calculation and applied strain-rate analysis further revealed that ultrasonic excitation en-hanced dislocation slipping,and enabled dislocation motion to accommodate severe plastic deformation at a high-strain-rate.
