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TBF 1180 steel was plastically deformed under different strain paths in order to study both the ductility and RA transformation rates. Specimens were prepared from a 1 mm thick sheet and then tested incrementally under uniaxial tension, plane-strain tension, and biaxial tension. The retained austenite (RA) levels were measured, as a function of the plastic strain, using electron backscatter diffraction (EBSD). The plane-strain tension specimens had the fastest rate of RA transformation as a function of strain, followed by uniaxial tension, and then biaxial tension. The forming limits were measured for each strain path, yielding major limit strains of 0.12 under uniaxial tension, 0.09 under plane-strain tension, and 0.16 under biaxial tension. These results were compared to prior work on a 1.2 mm Q&P 1180 steel sheet, which had a similar yield and ultimate tensile strength, but exhibited slightly greater forming limits than the TBF material. The visual inspection of the micrographs appeared to show an equiaxed RA morphology in the Q&P 1180 steel and a mixture of equiaxed and lamellar RA grains in the TBF 1180 steel. However, the statistics generated by EBSD revealed that both alloys had RA grains with essentially the same aspect ratios. The average RA grain size in the Q&P alloy was found to be about three times larger than that of the TBF alloy. As such, the small but consistent formability advantage exhibited by the Q&P 1180 alloy along all three strain paths can be attributed to its larger average RA grain size, where larger RA grain sizes correlated with a more gradual transformation rate. 

One of the major challenges towards understanding and further utilizing the properties and functional behaviors of grain boundaries (GB) is the complexity of general GBs with mixed tilt and twist character. Here, we report the correlations between mixed GBs and their tilt and twist components in terms of structure, energy and stress field by computationally examining 7440 silicon GBs. Such correlations indicate that low angle mixed GBs are formed through the reconstruction mechanisms between their superposed tilt and twist components, which are revealed as the energetically favorable dissociation, motion and reaction of dislocations and stacking faults. In addition, various complex disconnection network structures are discovered near the conventional twin and structural unit GBs, implying the role of disconnection superposition in forming high angle mixed GBs. By unveiling the energetic correlation, an extended Read-Shockley model that predicts the general trends of GB energy is proposed and confirmed in various GB structures across different lattices. Finally, this work is validated in comparison with experimental observations and first-principles calculations. 

This dataset provides segregation energy spectra information for cobalt solute in 7272 aluminum grain boundaries that span the 5D space of crystallographic character. The dataset and some of its characteristics are described in detail in https://doi.org/10.1016/j.actamat.2024.120448. The information about the segregation energy spectra are included in a CSV file. Each GB is identified by a computeID that is listed in the CSV file. The crystallographic character and selected properties for each GB, as well as its structure, are available in another dataset at https://doi.org/10.17632/4ykjz4ngwt, and which is described in an article at https://doi.org/10.1016/j.actamat.2022.118006. Note that the A README file provides a description of the columns of the CSV file. 

Solute segregation in materials with grain boundaries (GBs) has emerged as a popular method to thermodynamically stabilize nanocrystalline structures. However, the impact of varied GB crystallographic character on solute segregation has never been thoroughly examined. This work examines Co solute segregation in a dataset of 7272 Al bicrystal GBs that span the 5D space of GB crystallographic character. Considerable attention is paid to verification of the calculations in the diverse and large set of GBs. In addition, the results of this work are favorably validated against similar bicrystal and polycrystal simulations. As with other work, we show that Co atoms exhibit strong segregation to sites in Al GBs and that segregation correlates strongly with GB energy and GB excess volume. Segregation varies smoothly in the 5D crystallographic space but has a complex landscape without an obvious functional form. 

This dataset of 7304 aluminum grain boundaries provides comprehensive coverage of the 5D space of crystallographic character. The dataset and some of its characteristics are described in detail in https://doi.org/10.1016/j.actamat.2022.118006. The dataset here includes a zip file with all 7304 minimum energy grain boundary structure files, which are minimized dump files from LAMMPS. The dump files only include atoms +/- 15 angstroms from the grain boundary plane. The CSV file contains information about all 7304 grain boundaries, including information about the crystallographic character and a few computed properties. A README file provides a description of the columns of the CSV file. 

Abstract SPPARKS is an open-source parallel simulation code for developing and running various kinds of on-lattice Monte Carlo models at the atomic or meso scales. It can be used to study the properties of solid-state materials as well as model their dynamic evolution during processing. The modular nature of the code allows new models and diagnostic computations to be added without modification to its core functionality, including its parallel algorithms. A variety of models for microstructural evolution (grain growth), solid-state diffusion, thin film deposition, and additive manufacturing (AM) processes are included in the code. SPPARKS can also be used to implement grid-based algorithms such as phase field or cellular automata models, to run either in tandem with a Monte Carlo method or independently. For very large systems such as AM applications, the Stitch I/O library is included, which enables only a small portion of a huge system to be resident in memory. In this paper we describe SPPARKS and its parallel algorithms and performance, explain how new Monte Carlo models can be added, and highlight a variety of applications which have been developed within the code.