Themes: Beyond Similitude: Role of Multiscale Heterogeneity in Fracture Prognosis 

ANALYSIS OF RIGID CURVED INCLUSION EMBEDDED IN A SOFT MATRIX: EXPERIMENTAL INSIGHTS

The role of fiber curvature in short-fiber thermoplastics can be explored by studying a rigid curved inclusion embedded in an epoxy matrix. Although inclusion enhances global stiffness, it also acts as a source of stress singularity, which leads to failure. The current study employs the 2D digital image correlation (DIC) technique to obtain full-field strain fields over a rigid curved inclusion embedded in a soft matrix. The experiment is performed on a rigid curved inclusion specimen subjected to remote tensile loading of 350N. The experimentally obtained strain field is verified using the finite element technique, and a good match is observed. Finally, the stress intensity factor is defined for the rigid curved inclusion, and it is estimated along with the geometric correction factor.
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EXAMINING SUB-GRAIN DRIVING FORCES FOR SMALL CRACK GROWTH [Keynote]

High energy X-ray diffraction microscopy (HEDM) techniques and micro-computed tomography were combined with in-situ cyclic loading to examine the evolution of sub-grain-level fatigue crack growth within a Ni-base superalloy at room temperature. A focused-ion beam notch was introduced within the specimen to concentrate damage within the characterized microstructure region of interest. The test specimen was subjected to fatigue cycling with pauses for periodic micro-computed tomography and HEDM measurements to characterize the sporadic growth of the crack front and grain-level strains ahead of the crack front. The HEDM data was used to instantiate a crystal plasticity finite element model and compared to experimentally determined grain-level strains, sub-grain reorientation, and crack path.
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PREDICTING MICROSTRUCTURALLY SENSITIVE FATIGUE-CRACK PATH IN WE43 MAGNESIUM USING HIGH-FIDELITY NUMERICAL MODELING AND THREE-DIMENSIONAL EXPERIMENTAL CHARACTERIZATION

Microstructurally small fatigue-crack growth in polycrystalline materials is highly three-dimensional due to sensitivity to local microstructural features (e.g., grains). One requirement for modeling microstructurally sensitive crack propagation is establishing the criteria that govern crack evolution, including crack deflection. Here, a high-fidelity finite-element modeling framework is used to assess the performance and validity of various crack-growth criteria, including slip-based metrics (e.g., fatigue-indicator parameters), as potential criteria for predicting three-dimensional crack paths in polycrystalline materials. The modeling framework represents cracks as geometrically explicit discontinuities and involves voxel-based remeshing, mesh-gradation control, and a crystal-plasticity constitutive model. The predictions are compared to experimental measurements of WE43 magnesium samples subject to fatigue loading, for which three-dimensional grain structures and fatigue-crack surfaces were measured post-mortem using near-field high-energy X-ray diffraction microscopy and X-ray computed tomography. Findings from this work are expected to improve the predictive capabilities of numerical simulations involving microstructurally small fatigue-crack growth in polycrystalline materials.
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EFFECT OF LOCAL HETEROGENEITY ON FRACTURE DRIVING FORCES

Traditional fracture theories infer the local crack growth driving forces by surveying the mechanical response far from the crack. Although this approach has successfully predicted fracture by assuming isotropic and homogeneous materials, local heterogeneity such microstructural heterogeneity can affect fracture response. This presentation will evaluate the differences between the local and far field driving forces using different microstructure-sensitive modelling approaches. We will demonstrate the effects of grain size and crystallographic orientation gradients on crack tip blunting and microplasticity variability. We will also explore the role of microstructures as a buffer between the local and far fields considering the propagation of uncertainty from constitutive models into fracture prognosis. To conclude, we will discuss the implications for traditional experimental methods based on far field measurements smearing out important crack tip variability.
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CONSIDERATIONS ON THE R-CURVE OF HUMAN CORTICAL BONE

Human bone presents several factors which complicate the evaluation of fracture. Several toughening mechanisms protect humans from health complications, but also contribute to a unique 3D crack geometry. In this study, we combine 3D imaging, in-situ loading (in air and with a waterbath), and computational analysis for the interpretation of the toughness measurements of human cortical bone in the aging human population.
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COMPETITION BETWEEN INTERGRANULAR AND TRANSGRANULAR FAILURE IN ALUMINUM ALLOY: EXPERIMENTS AND CRYSTAL PLASTICITY MODELING

Aluminum alloys commonly used in airframe structures have been observed to show orthotropy in fracture when processed through hot rolling or extrusion, while other properties such as yield are more isotropic. Fracture orthotropy is likely due to a competition between damage accumulation within the grains by void growth and cleavage along the grain boundaries. Analysis of the fracture surface indicates varying degrees of dimpled regions (indicating damage by void growth) and quasi-brittle flat regions coinciding with the grain boundary (indicating grain boundary failure). To help determine structure-property relations in such materials, this paper describes a computational model for fracture in ductile polycrystals accounting for both the damage mechanisms. The model is validated by comparing with experiments on a high strength aluminum alloy, AA2139.
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USING DEEP LEARNING TO PREDICT MICROSTRUCTURALLY SMALL FATIGUE CRACK GROWTH PARAMETERS IN POLYCRYSTALLINE MATERIALS

The ability to rapidly predict the growth behavior of microstructurally small cracks (MSCs) has the potential to significantly advance fracture-based designs and structural prognosis. The difficulties associated with characterizing or predicting MSC growth using experimental and numerical techniques preclude the applicability of such techniques in industrial design approaches, despite their potential benefits. Here, we propose a framework to accelerate high-fidelity MSC growth predictions using deep-learning algorithms, viz. , convolutional neural networks (CNNs). The primary research aim is to train CNNs to predict the rules governing MSC growth and to subsequently apply the trained CNNs to make rapid forward predictions of local crack extension given microstructural neighborhood information along a crack front. The training data are acquired from a large number of “virtual” MSC growth observations enabled by high-fidelity finite-element-based simulations. The MSC-growth-simulation framework, data-extraction strategies, and application of deep-learning algorithms for data-driven model development will be presented, and the resulting advantages will be demonstrated.
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FORWARD AND INVERSE ANALYSIS OF TENSILE PROPETIES OF DUAL-PHASE STEELS

This study proposed a forward analysis method to predict tensile strength and total elongation by considering the three-dimensional microstructure of dual-phase steels. By repeating the forward analysis, an inverse analysis was performed to search for a microstructure with higher tensile properties. The optimal microstructures found by the inverse analysis were consistent with conventional materials engineering findings, demonstrating that the proposed inverse analysis method is effective in solving the structure-properties linkages in the inverse direction.
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INVESTIGATION OF HIERARCHICAL POROUS STRUCTURES USING PHASE-FIELD FRACTURE MODELING INFORMED BY MOLECULAR DYNAMICS SIMULATION [Keynote]

The mechanical integrity of hierarchical porous structures depends on their pore morphology. To investigate the role of pore morphology on the mechanical and fracture behaviors of these complex systems, a multi-scale approach has been proposed. This paper shows how molecular dynamics simulations provide the means to extract material properties at the atomistic scale to further inform phase-field fracture technique at the continuum scale in an attempt to understand the mechanical response of these porous materials.
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