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Magnesium (Mg) alloys are promising lightweight structural materials whose limited strength and room‐temperature ductility limit applications. Precise control of deformation‐induced twinning through microstructural alloy design is being investigated to overcome these deficiencies. Motivated by the need to understand and control twin formation during deformation in Mg alloys, a series of magnesium‐yttrium (Mg–Y) alloys are investigated using electron backscatter diffraction (EBSD). Analysis of EBSD maps produces a large dataset of microstructural information for >40000 grains. To quantitatively determine how processing parameters and microstructural features are correlated with twin formation, interpretable machine learning (ML) is employed to statistically analyze the individual effects of microstructural features on twinning. An ML classifier is trained to predict the likelihood of twin formation, given inputs including grain microstructural information and synthesis and deformation conditions. Then, feature selection is used to score the relative importance of these inputs for twinning in Mg–Y alloys. It is determined that using information only about grain size, grain orientation, and total applied strain, the ML model can predict the presence of twinning and that other parameters do not significantly contribute to increasing the model's predictive accuracy. Herein, the utility of ML for gaining new fundamental insights into materials processing is illustrated. 

Single-phase body-centered cubic (bcc) refractory medium- or high-entropy alloys can retain compressive strength at elevated temperatures but suffer from extremely low tensile ductility and fracture toughness. We examined the strength and fracture toughness of a bcc refractory alloy, NbTaTiHf, from 77 to 1473 kelvin. This alloy’s behavior differed from that of comparable systems by having fracture toughness over 253 MPa·m^1/2, which we attribute to a dynamic competition between screw and edge dislocations in controlling the plasticity at a crack tip. Whereas the glide and intersection of screw and mixed dislocations promotes strain hardening controlling uniform deformation, the coordinated slip of <111> edge dislocations with {110} and {112} glide planes prolongs nonuniform strain through formation of kink bands. These bands suppress strain hardening by reorienting microscale bands of the crystal along directions of higher resolved shear stress and continually nucleate to accommodate localized strain and distribute damage away from a crack tip. 

Refractory multi-principal element alloys exhibiting promising mechanical properties such as excellent strength retention at elevated temperatures have been attracting increasing attention. Although their inherent chemical complexity is considered a defining feature, a challenge arises in predicting local chemical ordering, particularly in grain boundary regions with an enhanced structural disorder. In this study, we use atomistic simulations of a large group of bicrystal models to sample a wide variety of interfacial sites (grain boundary) in NbMoTaW and explore emergent trends in interfacial segregation and the underlying structural and chemical driving factors. Sampling hundreds of bicrystals along the [001] symmetric tilt axis and analyzing more than one hundred and thirty thousand grain boundary sites with a variety of local atomic environments, we uncover segregation trends in NbMoTaW. While Nb is the dominant segregant, more notable are the segregation patterns that deviate from expected behavior and mark situations where local structural and chemical driving forces lead to interesting segregation events. For example, incomplete depletion of Ta in low-angle boundaries results from chemical pinning due to favorable local compositional environments associated with chemical short-range ordering. Finally, machine learning models capturing and comparing the structural and chemical features of interfacial sites are developed to weigh their relative importance and contributions to segregation tendency, revealing a significant increase in predictive capability when including local chemical information. Overall, this work, highlighting the complex interplay between the local grain boundary structure and chemical short-range ordering, suggests tunable segregation and chemical ordering by tailoring grain boundary structure in multi-principal element alloys.