Exploration of forward and inverse protocols for property optimization of Ti-6Al-4V

Finite element model;Crystal plasticity;Titanium alloys;High-throughput;Inverse design

Priddy, Matthew William ; McDowell, David L. Shih, Donald S. Mechanical Engineering Kalidindi, Surya R. Neu, Rick W. Garmestani, Hamid ; McDowell, David L.

University:Georgia Institute of Technology

Department:Mechanical Engineering

关键词: Finite element model;    Crystal plasticity;    Titanium alloys;    High-throughput;    Inverse design;   

Others  :  https://smartech.gatech.edu/bitstream/1853/55665/1/PRIDDY-DISSERTATION-2016.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

The modeling and simulation of advanced engineering materials undergoing mechanical loading requires accurate treatment of relevant microstructure features, such as grain size and crystallographic texture, to determine the heterogeneous response to deformation.However, many models constructed for this purpose are not being fully realized in their predictive capability.Additionally, physics-based models can be combined with bottom-up deductive mappings and top-down inductive decision paths to increase their utility in materials selection and optimization.However, connecting these types of models or algorithms with experiments, rapid inverse property/response estimates, and design decision-making via integrated workflows has yet to become well-established for materials design and/or development.One material system primed for this type of concurrent advancement is alpha+beta titanium alloys, because its resultant microstructure and mechanical properties are highly dependent on material processing and composition. This dissertation seeks to advance a materials design process for fatigue resistance, strength, and elastic stiffness of Ti-6Al-4V through the advancement of various computational tools, as well as the integration of simulation-based tools and high-throughput experimental datasets.The microstructure-sensitive crystal plasticity finite element method (CPFEM) is utilized to explicitly account for the grain structure and crystallographic texture of Ti-6Al-4V.To improve the predictive capability of the CPFEM model, high throughput spherical indentation experimental datasets are used for model calibration because of their ability to extract elastic and plastic individual phase and grain properties from multiphase materials such as titanium alloys.The CPFEM can be used to capture the microstructure heterogeneity on fatigue crack driving forces, but these types of simulations are computationally expensive.Instead, an explicit integration of the relevant constitutive relations in the CPFEM model are combined with the materials knowledge system (MKS) approach for generating spatially local results of polycrystalline materials.These bottom-up simulation methods provide macroscopic properties from microstructure-level model inputs.For materials design, it is important to determine the inverse -- microstructure-level information from the macroscopic response -- which is referred to as top-down modeling.The Inductive Design Exploration Method (IDEM) offers a systematic approach to combining bottom-up simulations with top-down inductive design search.In this dissertation, a generalized framework of the IDEM is implemented to assess multi-objective design scenarios specific to the microstructure-sensitive datasets generated in this work.Th e general approach presented in this dissertation integrates CPFEM simulations with experimental spherical indentation for model refinement and also combines CPFEM with the MKS for computational-efficient generation of local quantities.These advancements are the basis for accelerated decision-support for materials design exploration when merged with the IDEM.Although performed with alpha+beta titanium, individual elements of the framework can be applied to a variety of engineering alloys for tasks such as extraction of model parameters from spherical indentation experiments, coupling MKS with crystal plasticity constitutive relations, and performing a top-down inductive design search with polycrystalline datasets.