A Hierarchical Upscaling Method for Predicting Strength of Materials under Thermal, Radiation and Mechanical loading - Irradiation Strengthening Mechanisms in Stainless Steels

Stainless steels based on Fe-Cr-Ni alloys are the most popular structural materials used in reactors. High energy particle irradiation of in this kind of polycrystalline structural materials usually produces irradiation hardening and embrittlement. The development of predictive capability for the influence of irradiation on mechanical behavior is very important in materials design for next-generation reactors. Irradiation hardening is related to structural information crossing different length scale, such as composition, dislocation, crystal orientation distribution and so on. To predict the effective hardening, the influence factors along different length scales should be considered. A multiscale approach was implemented in this work to predict irradiation hardening of iron based structural materials. Three length scales are involved in this multiscale model: nanometer, micrometer and millimeter. In the microscale, molecular dynamics (MD) was utilized to predict on the edge dislocation mobility in body centered cubic (bcc) Fe and its Ni and Cr alloys. On the mesoscale, dislocation dynamics (DD) models were used to predict the critical resolved shear stress from the evolution of local dislocation and defects. In the macroscale, a viscoplastic self-consistent (VPSC) model was applied to predict the irradiation hardening in samples with changes in texture. The effects of defect density and texture were investigated. Simulated evolution of yield strength with irradiation agrees well with the experimental data of irradiation strengthening of stainless steel 304L, 316L and T91. This multiscale model we developed in this project can provide a guidance tool in performance evaluation of structural materials for next-generation nuclear reactors. Combining with other tools developed in the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the models developed will have more impact in improving the reliability of current reactors and affordability of new reactors

Inverse Microstructure and Processing Design and Homogenization

Presented on December 12, 2017 at 12:00 p.m. in the Marcus Nanotechnology Building, Rooms 1116-1118, Georgia Tech.Hamid Garmestani is a Professor of Materials Science and Engineering at Georgia Tech and is a Fellow of ASM International and ASME. He got his PhD from Cornell University in 1989, and after serving as a post-doctoral associate at Yale University, he joined FAMU-FSU college of Engineering as an assistant professor in Mechanical Engineering. Dr. Garmestani has had leadership roles in both the American Society of Mechanical Engineers Materials Division and ASM. He has organized more than 30 workshops and symposia in the emerging subject of materials design. He was awarded “Superstar in Research” by FSUCRC in 2000. He was also the recipient of the Faculty Award for Research from NASA. He is presently funded through DOE, Boeing, NOVELIS and NSF. Dr. Garmestani is a member of the editorial board of International Journal of Plasticity, Journal of Mechanics of Materials, Computers, Materials and Continua and Theoretical and Applied Multi-scale Modeling of Materials. He has developed methodologies in Microstructure Sensitive Design (MSD) framework that address an inverse methodology and innovations in various aspects of processing, structure-property relationships, and simulation-based design of materials.Runtime: 53:52 minutesThe field of materials and microstructure design and characterization techniques has progressed significantly in the past two decades. Materials and processing design methodologies effectively utilize the incomplete materials knowledgebase to link final product properties to initial microstructure. Microstructure representation has become a primary vehicle to reach this goal. Characterization techniques that can provide consistent microstructure representation include x-ray, microscopy (SEM, TEM), and tomography. Methodologies that can make the Inverse Materials Design a reality require novel mathematical and computational frameworks and methodologies in addition to experimentally-based knowledge creation to integrate computational-prediction and experimental-validation approaches. This talk will present current advances in multiscale computational materials frameworks based on Microstructure Sensitive Design and statistical homogenization techniques. Microstructure representation and digitization using spectral techniques are at the heart of such methodologies. Application of the present methodologies in thermo-mechanical processing of advanced magnesium alloys, the effect of machining in Al and Titanium alloys and processing of textured silicon solar cells and solid Oxide Fuel Cells are discussed with respect to inverse methodologies

Microstructure Sensitive Design and Processing in Solid Oxide Electrolyzer Cell

The aim of this study was to develop and inexpensive manufacturing process for deposition of functionally graded thin films of LSM oxides with porosity graded microstructures for use as IT-SOFCs cathode. The spray pyrolysis method was chosen as a low-temperature processing technique for deposition of porous LSM films onto dense YXZ substrates. The effort was directed toward the optimization of the processing conditions for deposition of high quality LSM films with variety of morphologies in the range of dense to porous microstructures. Results of optimization studies of spray parameters revealed that the substrate surface temperature is the most critical parameter influencing the roughness and morphology, porosity, cracking and crystallinity of the film

Prediction of Upper Surface Roughness in Laser Powder Bed Fusion

In this study, a physics-based analytical method was proposed for the prediction of upper surface roughness in laser powder bed fusion (LPBF). The temperature distribution and molten pool shape in the melting process were first predicted by an analytical thermal model. The cap area of the solidified molten pool was assumed to be half-elliptical. Based on this assumption and the principle of mass conservation, the cap height and the specific profile of the cap area were obtained. The transverse overlapping pattern of adjacent molten pools of upper layer was then obtained, with given hatch space. The analytical expression of the top surface profile was obtained after putting this overlapping pattern into a 2D coordinate system. The expression of surface roughness was then derived as an explicit function of the process parameters and material properties, based on the definition of surface roughness (Ra) in the sense of an arithmetic average. The predictions of surface roughness were then compared with experimental measurements of 316L stainless steel for validation and show acceptable agreement. In addition, the proposed model does not rely on numerical iterations, which ensures its low computational cost. Thus, the proposed analytical method can help understand the causes for roughness in LPBF and guide the optimization of process conditions to fabricate products with good quality. The sensitivity of surface roughness to process conditions was also investigated in this study

Prediction of Upper Surface Roughness in Laser Powder Bed Fusion

In this study, a physics-based analytical method was proposed for the prediction of upper surface roughness in laser powder bed fusion (LPBF). The temperature distribution and molten pool shape in the melting process were first predicted by an analytical thermal model. The cap area of the solidified molten pool was assumed to be half-elliptical. Based on this assumption and the principle of mass conservation, the cap height and the specific profile of the cap area were obtained. The transverse overlapping pattern of adjacent molten pools of upper layer was then obtained, with given hatch space. The analytical expression of the top surface profile was obtained after putting this overlapping pattern into a 2D coordinate system. The expression of surface roughness was then derived as an explicit function of the process parameters and material properties, based on the definition of surface roughness (Ra) in the sense of an arithmetic average. The predictions of surface roughness were then compared with experimental measurements of 316L stainless steel for validation and show acceptable agreement. In addition, the proposed model does not rely on numerical iterations, which ensures its low computational cost. Thus, the proposed analytical method can help understand the causes for roughness in LPBF and guide the optimization of process conditions to fabricate products with good quality. The sensitivity of surface roughness to process conditions was also investigated in this study

Multiphase Reconstruction of Heterogeneous Materials Using Machine Learning and Quality of Connection Function

Establishing accurate structure–property linkages and precise phase volume accuracy in 3D microstructure reconstruction of materials remains challenging, particularly with limited samples. This paper presents an optimized method for reconstructing 3D microstructures of various materials, including isotropic and anisotropic types with two and three phases, using convolutional occupancy networks and point clouds from inner layers of the microstructure. The method emphasizes precise phase representation and compatibility with point cloud data. A stage within the Quality of Connection Function (QCF) repetition loop optimizes the weights of the convolutional occupancy networks model to minimize error between the microstructure’s statistical properties and the reconstructive model. This model successfully reconstructs 3D representations from initial 2D serial images. Comparisons with screened Poisson surface reconstruction and local implicit grid methods demonstrate the model’s efficacy. The developed model proves suitable for high-quality 3D microstructure reconstruction, aiding in structure–property linkages and finite element analysis

Analytical Prediction of Molten Pool Dimensions in Powder Bed Fusion Considering Process Conditions-Dependent Laser Absorptivity

This research proposes an analytical method for the prediction of molten pool size in laser-based powder bed fusion (LPBF) additive manufacturing with the consideration of process conditions-dependent absorptivity. Under different process conditions, the melting modes in LPBF are different, which induces the difference in laser absorptivity. An empirical model of absorptivity was used to calculate the laser absorptivity under various process conditions. An analytical point-moving heat source model was employed to calculate the temperature distribution of the build-in LPBF, with absorptivity, material properties, and process conditions as inputs. The molten pool width, length, and depth were determined by comparing the predicted temperature profile with the melting temperature of the material. To validate the proposed method, the predicted molten pool width, and depth of Ti6Al4V were compared with the reported experimental measurements under various process conditions. The predicted molten pool widths were very close to the measured results, and the predictions of molten pool depth were also acceptable. The computational time of the presented model is less than 200s, which shows better computational efficiency than most methods based on numerical iterations, such as the finite element method (FEM). The sensitivity of molten pool width and depth to normalized enthalpy w also discussed. The presented analytical method can be a potential tool for the research of molten pool size and related defects in LPBF

Analytical Prediction of Molten Pool Dimensions in Powder Bed Fusion Considering Process Conditions-Dependent Laser Absorptivity

This research proposes an analytical method for the prediction of molten pool size in laser-based powder bed fusion (LPBF) additive manufacturing with the consideration of process conditions-dependent absorptivity. Under different process conditions, the melting modes in LPBF are different, which induces the difference in laser absorptivity. An empirical model of absorptivity was used to calculate the laser absorptivity under various process conditions. An analytical point-moving heat source model was employed to calculate the temperature distribution of the build-in LPBF, with absorptivity, material properties, and process conditions as inputs. The molten pool width, length, and depth were determined by comparing the predicted temperature profile with the melting temperature of the material. To validate the proposed method, the predicted molten pool width, and depth of Ti6Al4V were compared with the reported experimental measurements under various process conditions. The predicted molten pool widths were very close to the measured results, and the predictions of molten pool depth were also acceptable. The computational time of the presented model is less than 200s, which shows better computational efficiency than most methods based on numerical iterations, such as the finite element method (FEM). The sensitivity of molten pool width and depth to normalized enthalpy w also discussed. The presented analytical method can be a potential tool for the research of molten pool size and related defects in LPBF