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Predictive Modeling of Temperature and Grain Growth for a Thermally Processed Metal.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Predictive Modeling of Temperature and Grain Growth for a Thermally Processed Metal./
作者:	Allahua, Michael J.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	59 p.
附註:	Source: Masters Abstracts International, Volume: 82-02.
Contained By:	Masters Abstracts International82-02.
標題:	Materials science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27830409
ISBN:	9798662570122

Predictive Modeling of Temperature and Grain Growth for a Thermally Processed Metal.
Allahua, Michael J.

Predictive Modeling of Temperature and Grain Growth for a Thermally Processed Metal. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 59 p.

Source: Masters Abstracts International, Volume: 82-02.

Thesis (M.S.)--Rensselaer Polytechnic Institute, 2020.

This item must not be sold to any third party vendors.

This thesis will discuss work done towards microstructure control during thermo-mechanical processing of the titanium alloy Ti-6Al-4V. There are three main areas of research reported: thermal simulations, image processing, and grain growth fitting. These areas were investigated with the goal of linking processing to microstructure, which, in turn, dictates the properties of the material. As the microstructure evolves due to thermo-mechanical processes so do the properties. Thermal simulations were conducted to model the temperature distribution across a sample during thermal processing. Image processing was then used to determine the average grain size from SEM images collected during the thermal processing. Thus, this work provides a link between the thermal conditions modeled in the simulation and the local grain structure. Lastly, the grain size evolution was fit to the grain growth equation to understand the behavior of the grain growth. After thermal simulations were conducted, one notable finding was that the thermocouple attached to the top of the sample piece did not need to be modeled as it did not significantly impact the local temperature field. The code used for image processing, developed for grain boundary detection in single phase copper by C. J. Zheng [26], was found to perform well for Ti-6Al-4V, but requires trial and error for the selection of the thresholding parameter. Lastly, using grain growth data from the literature, studies and a methodology to fit the grain growth equation were made that can applied to forthcoming experimental data to support the development of grain growth control algorithms.

ISBN: 9798662570122Subjects--Topical Terms:

557839
Materials science.
Subjects--Index Terms:

Grain growth

Predictive Modeling of Temperature and Grain Growth for a Thermally Processed Metal.
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500 $a Source: Masters Abstracts International, Volume: 82-02.
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506 $a This item must not be sold to any third party vendors.
520 $a This thesis will discuss work done towards microstructure control during thermo-mechanical processing of the titanium alloy Ti-6Al-4V. There are three main areas of research reported: thermal simulations, image processing, and grain growth fitting. These areas were investigated with the goal of linking processing to microstructure, which, in turn, dictates the properties of the material. As the microstructure evolves due to thermo-mechanical processes so do the properties. Thermal simulations were conducted to model the temperature distribution across a sample during thermal processing. Image processing was then used to determine the average grain size from SEM images collected during the thermal processing. Thus, this work provides a link between the thermal conditions modeled in the simulation and the local grain structure. Lastly, the grain size evolution was fit to the grain growth equation to understand the behavior of the grain growth. After thermal simulations were conducted, one notable finding was that the thermocouple attached to the top of the sample piece did not need to be modeled as it did not significantly impact the local temperature field. The code used for image processing, developed for grain boundary detection in single phase copper by C. J. Zheng [26], was found to perform well for Ti-6Al-4V, but requires trial and error for the selection of the thresholding parameter. Lastly, using grain growth data from the literature, studies and a methodology to fit the grain growth equation were made that can applied to forthcoming experimental data to support the development of grain growth control algorithms.
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