This paper develops a Bayesian inference-based probabilistic crack nucleation model for the Ni-based superalloy René88DT under fatigue loading.A data-driven,machine learning approach is developed,identifying unde...This paper develops a Bayesian inference-based probabilistic crack nucleation model for the Ni-based superalloy René88DT under fatigue loading.A data-driven,machine learning approach is developed,identifying underlying mechanisms driving crack nucleation.An experimental set of fatigue-loaded microstructures is characterized near crack nucleation sites using scanning electron microscopy and electron backscatter diffraction images for correlating the grain morphology and crystallography to the location of crack nucleation sites.A concurrent multiscale model,embedding experimental polycrystalline microstructural representative volume elements(RVEs)in a homogenized material,is developed for fatigue simulations.The RVE domain is modeled by a crystal plasticity finite element model.An anisotropic continuum plasticity model,obtained by homogenization of the crystal plasticity model,is used for the exterior domain.A Bayesian classification method is introduced to optimally select informative state variable predictors of crack nucleation.From this principal set of state variables,a simple scalar crack nucleation indicator is formulated.展开更多
基金This work has been supported through Grant No.CMMI-1825115 from the National Science Foundation awarded by the Mechanics of Materials and Structures(MOMS)Program(Program Manager:Dr.Siddiq Qidwai)Early stages of this work was supported through a grant No.FA9550-12-1-0445 to the Center of Excellence on Integrated Materials Modeling(CEIMM)at Johns Hopkins University awarded by the AFOSR/RSL Computational Mathematics Program(Managers Dr.Fariba Fahroo and Dr.A.Sayir).
文摘This paper develops a Bayesian inference-based probabilistic crack nucleation model for the Ni-based superalloy René88DT under fatigue loading.A data-driven,machine learning approach is developed,identifying underlying mechanisms driving crack nucleation.An experimental set of fatigue-loaded microstructures is characterized near crack nucleation sites using scanning electron microscopy and electron backscatter diffraction images for correlating the grain morphology and crystallography to the location of crack nucleation sites.A concurrent multiscale model,embedding experimental polycrystalline microstructural representative volume elements(RVEs)in a homogenized material,is developed for fatigue simulations.The RVE domain is modeled by a crystal plasticity finite element model.An anisotropic continuum plasticity model,obtained by homogenization of the crystal plasticity model,is used for the exterior domain.A Bayesian classification method is introduced to optimally select informative state variable predictors of crack nucleation.From this principal set of state variables,a simple scalar crack nucleation indicator is formulated.
