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Results for “Ni-superalloy”

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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Microstructure Segmentation with Deep Learning Encoders Pre-Trained on a Large Microscopy Dataset

This study examined the improvement of microscopy segmentation accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures, including VGG, Inception, and ResNet, were trained on over 100,000 labelled microscopy images from 54 classes. These pre-trained encoders were then embedded into multiple segmentation architectures including U-Net and DeepLabV3+ to evaluate segmentation performance on newly created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2 percent reduction in relative segmentation error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.

machine learning↗
NASA NTRS2022

Microstructure Segmentation With Deep Learning Encoders Pre-Trained on a Large Microscopy Dataset

This study examined the improvement of microscopy segmentation intersection over union accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures were trained on over 100,000 labeled microscopy images from 54 material classes. These pre-trained encoders were then embedded into multiple segmentation architectures including UNet and DeepLabV3+ to evaluate segmentation performance on created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2% reduction in relative intersection over union error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.

machine learning↗
NASA NTRSDate not supplied

Twin nucleation and growth mechanism in Ni-based superalloys

While micro-twinning is the dominant creep deformation mechanism in Ni-based superalloys at temperatures above 700 C, many aspects of twin nucleation and growth remain unexplored. Kolbe mechanism for micro-twinning, based on thermally activated reordering, is probably the only concept currently widely accepted by the scientific community. We propose a qualitatively different mechanism for nucleation and growth of twins. The mechanism can be briefly described as follows. Penetration of a gamma prime precipitate by two 1/2(110) edge dislocations travelling on adjacent {111} glide planes triggers nucleation (at the interface of the precipitate and the matrix) and emission of Shockley partial of screw character on the glide plane of the edge dislocation, which entered the precipitate first (generating trailing high-energy anti-phase boundary (APB)). Propagation of this Shockley partial into the precipitate converts the APB into super intrinsic stacking fault (SISF). The recurring arrival of additional edge dislocations on the glide planes adjacent to the configuration described above leads to formation of super extrinsic stacking fault SESF, subsequent micro-twin formation, and growth of the twinned region. We demonstrate the proposed mechanism via molecular dynamics simulations.

Ni-superalloys↗