Výsledky vyhledávání - "Genevieve A. Kane"

The effect of cooling conditions on Ti 6%Al 4%V microstructure observed using high-temperature in-situ scanning electron microscopy

Autor: Robert Hull, Genevieve A. Kane, M. David Frey, Dustin Andersen

Publikováno v: Journal of Materials Research. 36:717-728

This study focuses on continuous imaging of microstructural changes in Ti 6% Al 4% V when cooling from the β transus of ~ 995 °C or above. A range of microstructures were obtained by accessing different cooling rates. With cooling rates of 0.1–0.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9c2e9c871ede293535e0e8dec4ca62be
https://doi.org/10.1557/s43578-020-00102-4

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Influence of Controlled Cooling Rates During Thermal Processing of Ti 6% Al 4% V Alloys Using In-Situ Scanning Electron Microscopy

Autor: M. David Frey, Genevieve A. Kane, Robert Hull

Publikováno v: MRS Advances. 5:1603-1611

We describe experimental approaches to real time examination of the microstructural evolution of Ti 6%Al 4%V upon cooling from above the beta transus (~995 °C) while imaging in the scanning electron microscope. Ti 6%Al 4%V is a two phase, α+β tita

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::485bfa8ed3830dab201b6498af46fdea
https://doi.org/10.1557/adv.2020.190

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Substrates with Programmable Heater Arrays for In-Situ Observation of Microstructural Evolution of Polycrystalline Films: Towards Real Time Control of Grain Growth

Autor: John T. Wen, Chengjian Zheng, Prabhu Balasubramanian, Genevieve A. Kane, Robert Hull, Antoinette M. Maniatty, Yixuan Tan

Publikováno v: MRS Advances. 1:1947-1952

An integrated experimental – simulation – control theory approach designed to enable adaptive control of microstructural evolution in polycrystalline metals is described. A micro-heater array, containing ten addressable channels, is used to creat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c53d57cc3243e325a5efef0e1c9f457c
https://doi.org/10.1557/adv.2016.236

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Adaptive characterization of microstructure dataset using a two stage machine learning approach

Autor: Robert Hull, Arun Baskaran, Genevieve A. Kane, Daniel Lewis, Krista Biggs

Publikováno v: Computational Materials Science. 177:109593

One of the goals of Materials Informatics (generally) and Image Driven Machine Learning (specifically) is extraction of quantitative data from micrographs towards an efficient characterization of microstructural features. Towards this goal, we report

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::699438482c831c2a952aa7482cc36cd5
https://doi.org/10.1016/j.commatsci.2020.109593

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