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Inelastic mechanical responses in solids, such as plasticity, damage and crack initiation, are typically modeled in constitutive ways that display microstructural and loading dependence. Nevertheless, linear elasticity at infinitesimal deformations is used for microstructural properties. We demonstrate a framework that builds on sequences of microstructural images to develop fingerprints of inelastic tendencies, and then use them for data-rich predictions of mechanical responses up to failure. In analogy to common fingerprints, we show that these two-dimensional instability-precursor signatures may be used to reconstruct the full mechanical response of unknown sample microstructures; this feat is achieved by reconstructing appropriate average behaviors with the assistance of a deep convolutional neural network that is fine-tuned for image recognition. We demonstrate basic aspects of microstructural fingerprinting in a toy model of dislocation plasticity and then, we illustrate the method’s scalability and robustness in phase field simulations of model binary alloys under mode-I fracture loading. 

Crack initiation emerges due to a combination of elasticity, plasticity, and disorder, and it displays strong dependence on the material’s microstructural details. The characterization of the structural uncertainty in the original microstructure is typically empirical and systematic characterization protocols are lacking. In this paper, we propose an investigational tool in the form of the curvature an ellipsoidal notch: As the radius of curvature at the notch increases, there is a dynamic phase transition from notch-induced crack initiation to bulk-disorder crack nucleation. The notch length scale associated with this transition may provide an additional characteristic of the original material microstructure. We investigate brittle but elastoplastic metals with coarse-grained, microstructural disorder that could originate in a material’s manufacturing process, such as alloying. We perform extensive and realistic simulations using a phase-field approach coupled to crystal plasticity. The microstructural disorder and notch width are systematically varied. We identify this transition for various disorder strengths in terms of the damage evolution. We identify detectable precursors to crack initiation that we quantify in terms of the expected stress drops during mode I fracture loading. Finally, we discuss ways to observe and analyze this brittle to quasi-brittle transition in experiments. 

This study investigates the statistical significance of crack jump noise in Inconel 718 (IN718) for several different loading conditions. A direct current potential drop (DCPD) method is used to experimentally measure in-situ the crack length. Data is collected for six different peak loads at R=0.15 for a statistically significant number of trails. FEA-derived calibration curves relate measured potential to crack length. We determine that the mean crack length jumps, over subsequent cycles, increased with loading, the range of the crack length jump distributions decreases with increasing load, while the noise has a non-zero mean distribution. Findings from this study suggest that crack length jumps are not random events but contain statistical features that can potentially be used with machine learning approaches to better understand fatigue progression in Ni-based superalloys.