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Results for “substructuring”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Intra-variant substructure in Ni–Mn–Ga martensite: Conjugation boundaries

The microstructure of a Ni–Mn–Ga alloy in the martensitic phase was investigated using transmission electron microscopy. Inter-variant twin boundaries were observed separating non-modulated tetragonal martensite variants. In addition, intra-variant boundary structures, referred to here as “conjugation boundaries”, were also observed. We propose that conjugation boundaries originate at the transformation interface between austenite and a nascent martensite variant. In the alloy studied, deformation twinning was observed, consistent with being the mode of lattice-invariant deformation, and this can occur on either of two crystallographically equivalent conjugate View the MathML source{101}(101⁻) twinning systems: conjugation boundaries separate regions within a single variant in which the active modes were distinct. The defect structure of conjugation boundaries and the low-angle of misorientation across them are revealed in detail using high-resolution microscopy. Finally, we anticipate that the mobility of such boundaries is lower than that of inter-variant boundaries, and is therefore likely to significantly affect the kinetics of deformation in the martensitic phase.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2019

On the measurement of dislocations and dislocation substructures using EBSD and HRSD techniques

The accumulation of the dislocations and development of dislocation structures in plastically deformed Ni201 is examined using dedicated analyses of Electron Back-Scatter Diffraction (EBSD) acquired orientation maps, and High-Resolution Synchrotron Diffraction (HRSD) acquired patterns. The results show that the minimum detectable microstructure-averaged (bulk) total dislocation density ($ρ_T$) measured via HRSD is approximately 1E13 m -2 , while the minimum GND density ($ρ_G$) measured via EBSD is approximately 2E12 m -2 - the EBSD technique being more sensitive at low plastic strain. This highlights complementarity of the two techniques when attempting to quantify amount of plastic deformation (damage) in a material via a measurement of present dislocations and their structures. Furthermore, a relationship between EBSD-measured $ρ_G$ and the size of HRSD-measured Coherently Scattering Domains (CSDs) has been mathematically derived - this allows for an estimation of the size of CSDs from EBSD-acquired orientation maps, and conversely an estimation of $ρ_G$ from HRSD-measured size of CSDs. The measured evolution of ($ρ_T$), and $ρ_G$ is compared with plasticity theory models - the current results suggest that Ashby's single-slip model underestimates the amount of ONDs ($ρ_G$), while Taylor's model is correctly predicting the total amount of dislocation ($ρ_T$) present in the material as a function of imparted plastic strain.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Receptance coupling substructure analysis and chatter frequency-informed machine learning for milling stability

This paper describes a milling stability identification approach that simultaneously considers: physics-based models for the tool tip frequency response functions and stability predictions; the binary result from a milling test (automatically labeled as stable or unstable based on frequency content); chatter frequency when an unstable result is obtained; and user risk tolerance. The algorithm applies probabilistic Bayesian machine learning with adaptive, parallelized Markov Chain Monte Carlo sampling to update the probability of stability with each milling test. Furthermore, the result is a robust solution for rapid convergence to optimized milling parameters for maximum metal removal rate using all available information.

42 ENGINEERING↗
DOE OSTI2018

Subnanometer Substructures in Nanoassemblies Formed from Clusters under a Reactive Atmosphere Revealed Using Machine Learning

Size-selected clusters, soft-landed on an oxide substrate, is a promising and highly tunable material for heterogeneous catalysis. Agglomeration of the deposited clusters, however, leads to changes in the particle properties and structure. The latter for such cluster assemblies can also be different from that in self standing nanoparticles of similar sizes. To monitor the formation of such complex materials, in situ studies at different length scales are required. Toward that goal, we combined small-angle X-ray scattering (SAXS), X-ray absorption near-edge structure (XANES) spectroscopy, ab initio simulations, and machine learning (artificial neural network) techniques. We detected significant differences between the sizes of particle agglomerates, as probed by SAXS, and the sizes of locally ordered regions, as seen by XANES. We interpret these differences as an evidence for the fractal, grape-cluster-like structure of the agglomerates; thus, XANES and SAXS provide highly complementary structural information. This finding can have a profound effect on our understanding of particle sintering and assembly processes and of structure properties relationship in ultradispersed metal catalysts in reaction conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗