The proposal develops a comprehensive additive manufacturing (AM) metal-powder Grain-Boundary-Engineering (GBE) toolset to improve material mechanical, fracture, and fatigue properties. Phase I developed an Integrated Computational Material engineering (ICME) physics-based tool set to implement GBE modelling to improve microstructure of an AM polycrystalline material and to optimize the microstructure from Equiaxed to coincidence-site-lattice (CSL) grain boundaries, low-angle-grain-boundaries (LAGB) grains, with/without nano-inclusions. Phase I demonstrated and test validated the feasibility of AM build of polycrystalline stainless-steel with improved strength and strain. Phase II will further improve ICME software to reduce Trial-Error in AM process, reduce cycle time for part qualification, and accelerate materials development by establishing AM Digital Twin. AM creates several complex thermal processes which alter mechanical properties in terms of strength and plasticity, as the result of a materials microstructure changes. GBE by multi-scale modelling will be performed to expedite qualification process for existing and new AM polycrystalline alloys. Phase II will expand the Phase I findings from steel to a high temperature polycrystalline super alloy that exhibits crack and weldability issues. Successful ICME based Optimized CSL/LAGB during AM by thermal-heating, fast-cooling, melt-excitation, and inclusion techniques will entail: (i) Micro-thermal-management to determine the thermal-history (melt-pool depth/width, superheated-cooling), Material state (voids/density), %crystallization, process-map of stable and unstable print regions, (ii) Nano-Micro-mechanical analytical modeling, predicting mechanical properties (stresses-strain), layers distortion/curvature, considering inclusion, defects and uncertainties. (iii) grain-boundary-modeling (GBM) using creep-diffusion algorithms, predicting surface-roughness, residual stress/strain, cracks (inter-granular/trans-granular), oxidation, (iv) visualization of 3D Voxel Electron-back-scatter-diffraction (EBSD) image, and (v) progressive-failure-analysis of improved mechanical properties (failure-strain, yield/ultimate strength). ICME based Design of Experiment (DOE) Framework Optimization will be used to determine AM machine parameters, alloy composition, temperature-precipitates (TTP) formation for improved ductility, strength, toughness, fatigue, and weldability. A building block prediction validation strategy will be implemented on AM design, build, and test of: a) Non critical Stainless steel bracket using EOS/Concept-laser LPBF AM Machines, and b) critical Inconel 738 turbine blade using ARCAM EBM AM machine. ICME driven design will be compared/validated with tests including: a) printing specimens using machine equipped in-Situ-monitored sensors, and NDE measurement, failure-strains, and b) use of SEM/TEM microscopy, Electron-beam-scatter-diffraction (EBSD) imaging.
