Plastic instability,including both the discontinuous yielding and stress serrations,has been frequently observed during the tensile deformation of medium-Mn steels(MMnS)and has been intensively studied in recent years...Plastic instability,including both the discontinuous yielding and stress serrations,has been frequently observed during the tensile deformation of medium-Mn steels(MMnS)and has been intensively studied in recent years.Unfortunately,research results are controversial,and no consensus has been achieved regarding the topic.Here,we first summarize all the possible factors that affect the yielding and flow stress serrations in MMnS,including the morphology and stability of austenite,the feature of the phase interface,and the deformation parameters.Then,we propose a universal mechanism to explain the conflicting experimental results.We conclude that the discontinuous yielding can be attributed to the lack of mobile dislocation before deformation and the rapid dislocation multiplication at the beginning of plastic deformation.Meanwhile,the results show that the stress serrations are formed due to the pinning and depinning between dislocations and interstitial atoms in austenite.Strain-induced martensitic transformation,influenced by the mechanical stability of austenite grain and deformation parameters,should not be the intrinsic cause of plastic instability.However,it can intensify or weaken the discontinuous yielding and the stress serrations by affecting the mobility and density of dislocations,as well as the interaction between the interstitial atoms and dislocations in austenite grains.展开更多
Fast prediction of microstructural responses based on realistic material topology is vital for linking process,structure,and properties.This work presents a digital framework for metallic materials using microscale fe...Fast prediction of microstructural responses based on realistic material topology is vital for linking process,structure,and properties.This work presents a digital framework for metallic materials using microscale features.We explore deep learning for two primary goals:(1)segmenting experimental images to extract microstructural topology,translated into spatial property distributions;and(2)learning mappings from digital microstructures to mechanical fields using physics-informed operator learning.Loss functions are formulated using discretized weak or strong forms,and boundary conditions-Dirichlet and periodic-are embedded in the network.Input space is reduced to focus on key features of 2D and 3D materials,and generalization to varying loads and input topologies are demonstrated.Compared to FEM and FFT solvers,our models yield errors under 1–5%for averaged quantities and are over 1000×faster during 3D inference.展开更多
基金support from the National Natural Science Foundation of China(Nos.51831002,51904028,and 52233018)the Beijing Municipal Natural Science Foundation(No.2242048)the Fundamental Research Funds for the Central Universities,China(No.FRF-EYIT-23-08).
文摘Plastic instability,including both the discontinuous yielding and stress serrations,has been frequently observed during the tensile deformation of medium-Mn steels(MMnS)and has been intensively studied in recent years.Unfortunately,research results are controversial,and no consensus has been achieved regarding the topic.Here,we first summarize all the possible factors that affect the yielding and flow stress serrations in MMnS,including the morphology and stability of austenite,the feature of the phase interface,and the deformation parameters.Then,we propose a universal mechanism to explain the conflicting experimental results.We conclude that the discontinuous yielding can be attributed to the lack of mobile dislocation before deformation and the rapid dislocation multiplication at the beginning of plastic deformation.Meanwhile,the results show that the stress serrations are formed due to the pinning and depinning between dislocations and interstitial atoms in austenite.Strain-induced martensitic transformation,influenced by the mechanical stability of austenite grain and deformation parameters,should not be the intrinsic cause of plastic instability.However,it can intensify or weaken the discontinuous yielding and the stress serrations by affecting the mobility and density of dislocations,as well as the interaction between the interstitial atoms and dislocations in austenite grains.
基金the funding support provided to develop the present work in the project Cluster of Excellence“Internet of Production”(project:390621612).
文摘Fast prediction of microstructural responses based on realistic material topology is vital for linking process,structure,and properties.This work presents a digital framework for metallic materials using microscale features.We explore deep learning for two primary goals:(1)segmenting experimental images to extract microstructural topology,translated into spatial property distributions;and(2)learning mappings from digital microstructures to mechanical fields using physics-informed operator learning.Loss functions are formulated using discretized weak or strong forms,and boundary conditions-Dirichlet and periodic-are embedded in the network.Input space is reduced to focus on key features of 2D and 3D materials,and generalization to varying loads and input topologies are demonstrated.Compared to FEM and FFT solvers,our models yield errors under 1–5%for averaged quantities and are over 1000×faster during 3D inference.
