- Award ID(s):
- 1727495
- PAR ID:
- 10111665
- Date Published:
- Journal Name:
- Journal of Materials Research
- Volume:
- 33
- Issue:
- 22
- ISSN:
- 0884-2914
- Page Range / eLocation ID:
- 3711 to 3738
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
A novel dislocation-density-based crystal plasticity model for nanocrystalline face-centered cubic metals is developed based on the thermally-activated mechanism of dislocations depinning from grain boundaries. Dislocations nucleated from grain boundary dislocation sources are assumed to be the primary carriers of plasticity in the nanocrystals. The evolution of the dislocation density thereby involves a competition between the nucleation of dislocations from grain boundary defect structures, such as ledges, and the absorption of dislocations into the grain boundary via diffusion processes. This model facilitates the simulation of plastic deformation in nanocrystalline metals, with consideration of the initial microstructure resulting from a particular processing method, to be computed as a direct result of dislocation-mediated plasticity only. The exclusion of grain boundary-mediated plasticity mechanisms in the formulation of the crystal plasticity model allows for the exploration of the fundamental role dislocations play in nanocrystalline plasticity. The combined effect of average grain size, grain size distribution shape, and initial dislocation density on the mechanical performance and strain-rate sensitivity are explored with the model. Further, the influence of the grain boundary diffusivity on post-yielding strain-hardening behavior is investigated to discern the impact that the choice of processing route has on the resulting deformation response of the material.more » « less
-
Recent advances pertaining to modeling of grain fragmentation during deformation and recrystallization of polycrystalline metals using viscoplastic self-consistent (VPSC) polycrystal plasticity are combined into a field fluctuations VPSC (FF-VPSC) model. The FF-VPSC model is a higher-order formulation calculating the second moments of lattice rotation rates based on the second moments of stress fields inside grains and resulting intragranular misorientation distributions. The misorientation distributions are used to define a grain fragmentation sub-model for improving predictions of deformation texture evolution and to formulate kinetics sub-models for nucleation as well as to influence the stored energy governing grain growth for the predictions of recrystallization texture evolution. Formation of a copper-like texture in moderately high stacking fault energy (SFE) Cu and a brass-like texture in low SFE brass during rolling to very large strains are successfully predicted using the model. Remarkably, the model also predicts recrystallization textures from the deformation textures of the two metals after adjusting tradeoffs between transition-bands and grain boundary nucleation mechanisms. Additionally, rolling and recrystallization of an interstitial-free steel, tension and recrystallization of AA5182-O, and recrystallization of an additively manufacturing cobalt-based alloy MarM-509 are simulated to predict texture evolution. Through these case studies involving multiple alloys and thermo-mechanical processes we show that, in addition to being predictive with good accuracy, the key advantage of the model lies in its versatility. The FF-VPSC model, simulation results, and insights from the results are presented and discussed in this paper.more » « less
-
Microstructure-sensitive materials design has become popular among materials engineering researchers in the last decade because it allows the control of material performance through the design of microstructures. In this study, the microstructure is defined by an orientation distribution function. A physics-informed machine learning approach is integrated into microstructure design to improve the accuracy, computational efficiency, and explainability of microstructure-sensitive design. When data generation is costly and numerical models need to follow certain physical laws, machine learning models that are domain-aware perform more efficiently than conventional machine learning models. Therefore, a new paradigm called the physics-informed neural network (PINN) is introduced in the literature. This study applies the PINN to microstructure-sensitive modeling and inverse design to explore the material behavior under deformation processing. In particular, we demonstrate the application of PINN to small-data problems driven by a crystal plasticity model that needs to satisfy the physics-based design constraints of the microstructural orientation space. For the first problem, we predict the microstructural texture evolution of copper during a tensile deformation process as a function of initial texturing and strain rate. The second problem aims to calibrate the crystal plasticity parameters of the Ti-7Al alloy by solving an inverse design problem to match the PINN-predicted final texture prediction and the experimental data.
-
Microstructure-sensitive material design has become popular among materials engineering researchers in the last decade because it allows the control of material performance through the design of microstructures. In this study, the microstructure is defined by an orientation distribution function (ODF). A physics-informed machine learning approach is integrated into microstructure design to improve the accuracy, computational efficiency, and explainability of microstructure-sensitive design. When data generation is costly and numerical models need to follow certain physical laws, machine learning models that are domain-aware perform more efficiently than conventional machine learning models. Therefore, a new paradigm called Physics-Informed Neural Network (PINN) is introduced in the literature. This study applies the PINN to microstructure-sensitive modeling and inverse design to explore the material behavior under deformation processing. In particular, we demonstrate the application of PINN to small-data problems driven by a crystal plasticity model that needs to satisfy the physics-based design constraints of the microstructural orientation space. For the first problem, we predict the microstructural texture evolution of Copper during a tensile deformation process as a function of initial texturing and strain rate. The second problem aims to calibrate the crystal plasticity parameters of Ti-7Al alloy by solving an inverse design problem to match PINN-predicted final texture prediction and the experimental data.more » « less
-
Abstract The present work utilizes Orientation Imaging Microscopy and Finite Element Modelling to analyse microstructure evolution in grains near defects during plane strain indentation of direct metal laser sintered Inconel 718. Defects are inevitably produced during printing of metals and they degrade the mechanical behaviour of parent components. Understanding microstructure evolution of grains present near defects can help create better predictive models of mechanical behaviour of components resulting from additive manufacturing. In this work, an ex-situ study of microstructure evolution during plane strain indentation of DMLS Inconel 718 specimens is performed. Regions that lie near volumetric porosity defects were studied. Grain Orientation Spread was utilized as a metric to quantify intra-granular deformation. It was seen that microstructure evolution of grains near defects is enhanced due to strain concentrations whereby they exhibit larger orientation spread after plastic deformation. Finite Element Analysis was used to simulate the plane strain indentation test on the specimen in which, porosity defects and roughness textures similar to those seen in the as-received specimen were programmed using the python scripting interface of Abaqus. Results from finite element analysis were compared with insights from microstructure analysis to describe evolution of microstructure during deformation near defects.