Computational models, including physics-based models such as the crystal plasticity finite element method (CPFEM), and top-down preliminary design search algorithms like the Inductive Design Exploration Method (IDEM), are increasingly being fused with experimental methods to increase the pace of decision support in materials design and development. However, connecting these types of models with experiments, rapid inverse property/response estimates, and design decision-making via integrated workflows has yet to become well-established in the materials community for certain scientific and engineering challenges.
This proposal highlights previous studies and outlines a research plan, to be executed in the context of a NSF GOALI program, to implement a simulation-based workflow that aids the materials design exploration process for fatigue resistance, strength, and elastic stiffness of Ti-6Al-4V, an α+β Ti alloy, with the use of CPFEM models and the IDEM methodology. Recently, spherical nanoindentation (SNI) experiments conducted within the group of S. Kalidindi have demonstrated an ability to extract individual phase and grain properties. Additionally, the materials knowledge system (MKS) approach to rapid inverse design using data science methods has proven to be extremely computationally efficient for generating spatially local results of polycrystalline materials. The proposed project will integrate these capabilities to provide improved CPFEM models and validated accelerated decision support for materials design exploration. The significance of this work includes:
1. The coupling of CPFEM simulations of the SNI process with experiments (conducted by a collaborator) for parameter extraction and evaluation of model validity and form for individual phase and interface properties/responses.
2. The development of constitutive relations that account for the slip resistance associated with the presence of the α/β interface.
3. The extension of the CPFEM model and fatigue simulation results for MKS model calibration performed by a collaborator.
4. The application of IDEM to CPFEM and accelerated MKS-generated datasets for both local (grain level) and global (polycrystalline) properties/responses to pursue robust material designs for ranged sets of specified performance objectives.