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Modeling of microstructure property ...

Tiley, Jaimie Scott.

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Modeling of microstructure property relationships in titanium-aluminum-vanadium.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling of microstructure property relationships in titanium-aluminum-vanadium./
Author:	Tiley, Jaimie Scott.
Description:	192 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-04, Section: B, page: 1869.
Contained By:	Dissertation Abstracts International64-04B.
Subject:	Engineering, Materials Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3088893

Modeling of microstructure property relationships in titanium-aluminum-vanadium.
Tiley, Jaimie Scott.

Modeling of microstructure property relationships in titanium-aluminum-vanadium. - 192 p.

Source: Dissertation Abstracts International, Volume: 64-04, Section: B, page: 1869.

Thesis (Ph.D.)--The Ohio State University, 2003.

Fuzzy logic neural network models were developed to predict the room temperature tensile behavior of Ti-6Al-4V. This involved the development of a database relating microstructure to properties. This necessitated establishing heat treatment processes to develop microstructural features, mechanical testing of samples, creating rigorous stereology procedures, developing numerical models to predict mechanical behavior, and determining trends and inter-relationships relating microstructural features to mechanical properties. Microstructural features were developed using a Gleeble(TM) 1500 Thermal-mechanical simulator. Samples were obtained from mill annealed plate material and both alpha + beta forged and beta forged materials. A total of 72 samples were beta solutionized and heat treated using different heating and cooling conditions.Subjects--Topical Terms:

1017759
Engineering, Materials Science.

Modeling of microstructure property relationships in titanium-aluminum-vanadium.
LDR:03330nmm 2200301 4500 001 1865272
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008 130614s2003 eng d
035 $a (UnM)AAI3088893
035 $a AAI3088893
040 $a UnM $c UnM
100 1 $a Tiley, Jaimie Scott. $3 1952726
245 1 0 $a Modeling of microstructure property relationships in titanium-aluminum-vanadium.
300 $a 192 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-04, Section: B, page: 1869.
500 $a Adviser: Hamish Fraser.
502 $a Thesis (Ph.D.)--The Ohio State University, 2003.
520 $a Fuzzy logic neural network models were developed to predict the room temperature tensile behavior of Ti-6Al-4V. This involved the development of a database relating microstructure to properties. This necessitated establishing heat treatment processes to develop microstructural features, mechanical testing of samples, creating rigorous stereology procedures, developing numerical models to predict mechanical behavior, and determining trends and inter-relationships relating microstructural features to mechanical properties. Microstructural features were developed using a Gleeble(TM) 1500 Thermal-mechanical simulator. Samples were obtained from mill annealed plate material and both alpha + beta forged and beta forged materials. A total of 72 samples were beta solutionized and heat treated using different heating and cooling conditions.
520 $a Rigorous stereology procedures were developed to characterize the important microstructural features. The features included Widmanstatten alpha lath thickness, volume fraction of total alpha, volume fraction of Widmanstatten alpha, grain boundary alpha thickness, mean edge length, colony scale factor, and prior beta grain size factor. Chemical composition was also determined using standard chemical analysis and microscopy techniques. The samples were tested for yield strength, ultimate tensile strength, and elongation at room temperature.
520 $a Results from the tests and the characterization were used to develop fuzzy logic neural network models to predict the mechanical behaviors and develop relationships between the microstructural features (using CubiCalc RTC(TM)). Results were compared to standard multi-variable regression models. The fuzzy logic neural network models were able to predict the yield, and ultimate tensile strength, within acceptable error ranges with a limited number of input data samples. The models also predicted the elongation values but with larger errors.
520 $a Of particular importance, the models identified the importance of the Widmanstatten alpha lath widths, the mean edge length of the Widmanstatten alpha laths, the colony scale factor, and the prior beta grain size to the tensile behavior. The trends also identified the inter-relationship between the microstructural features. Chemical composition data for the primary alloying elements and interstitials was also determined to help explain the results in terms of traditional metallurgy.
590 $a School code: 0168.
650 4 $a Engineering, Materials Science. $3 1017759
650 4 $a Engineering, Metallurgy. $3 1023648
690 $a 0794
690 $a 0743
710 2 0 $a The Ohio State University. $3 718944
773 0 $t Dissertation Abstracts International $g 64-04B.
790 1 0 $a Fraser, Hamish, $e advisor
790 $a 0168
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3088893