Relational linkages connecting process,structure,and properties are some of the most sought after goals in additive manufacturing(AM).This is desired especially because the microstructural grain morphologies of AM com...Relational linkages connecting process,structure,and properties are some of the most sought after goals in additive manufacturing(AM).This is desired especially because the microstructural grain morphologies of AM components can be vastly different than their conventionally manufactured counterparts.Furthermore,data collection at the microscale is costly.Consequently,this work describes and demonstrates a methodology to link microstructure morphology to mechanical properties using functional Gaussian process surrogate models in a directed graphical network capable of achieving near real-time property predictions with single digit error magnitudes when predicting full stress–strain histories of a given microstructure.This methodology is presented and demonstrated using computationally generated microstructures and results from crystal plasticity simulations on those microstructures.The surrogate model uses grain-level microstructural descriptors rather than whole microstructure descriptors so that properties of new,arbitrary microstructures can be predicted.The developed network has the potential to scale to predict mechanical properties of grain structures that would be infeasible to simulate using finite element methods.展开更多
基金R.S.,J.M.,and A.B.acknowledge partial support for this work by the Office of Naval Research through the Naval Research Laboratory’s(NRL)core fundingR.S.acknowl-edges partial support for this work by the NRL Edison Graduate Memorial Training ProgramThis work was supported in part by high-performance computer time and resources from the DoD High Performance Computing Modernization Program.
文摘Relational linkages connecting process,structure,and properties are some of the most sought after goals in additive manufacturing(AM).This is desired especially because the microstructural grain morphologies of AM components can be vastly different than their conventionally manufactured counterparts.Furthermore,data collection at the microscale is costly.Consequently,this work describes and demonstrates a methodology to link microstructure morphology to mechanical properties using functional Gaussian process surrogate models in a directed graphical network capable of achieving near real-time property predictions with single digit error magnitudes when predicting full stress–strain histories of a given microstructure.This methodology is presented and demonstrated using computationally generated microstructures and results from crystal plasticity simulations on those microstructures.The surrogate model uses grain-level microstructural descriptors rather than whole microstructure descriptors so that properties of new,arbitrary microstructures can be predicted.The developed network has the potential to scale to predict mechanical properties of grain structures that would be infeasible to simulate using finite element methods.
