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Investigating the Relationships Between Material Properties and Microstructural Shapes as Quantified by Moment Invariants.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Investigating the Relationships Between Material Properties and Microstructural Shapes as Quantified by Moment Invariants./
Author:	Harrison, Ryan K.S.
Description:	1 online resource (216 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Contained By:	Dissertation Abstracts International79-09B(E).
Subject:	Materials science. -
Online resource:	click for full text (PQDT)
ISBN:	9780355952506

Investigating the Relationships Between Material Properties and Microstructural Shapes as Quantified by Moment Invariants.
Harrison, Ryan K.S.

Investigating the Relationships Between Material Properties and Microstructural Shapes as Quantified by Moment Invariants. - 1 online resource (216 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2018.

Includes bibliographical references

The analysis of microstructural shapes is an underutilized tool in the field of materials science. Typical observations of morphology are qualitative, rather than quantitative, which prevents the identification of relationships between shape and the mechanical properties of a material. Recent advances in the fields of computer vision and high-dimensional analysis have made computer-based shape characterization feasible on a variety of materials.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355952506Subjects--Topical Terms:

557839
Materials science.
Index Terms--Genre/Form:

554714
Electronic books.

Investigating the Relationships Between Material Properties and Microstructural Shapes as Quantified by Moment Invariants.
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245 1 0 $a Investigating the Relationships Between Material Properties and Microstructural Shapes as Quantified by Moment Invariants.
264 0 $c 2018
300 $a 1 online resource (216 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
500 $a Adviser: Marc De Graef.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2018.
504 $a Includes bibliographical references
520 $a The analysis of microstructural shapes is an underutilized tool in the field of materials science. Typical observations of morphology are qualitative, rather than quantitative, which prevents the identification of relationships between shape and the mechanical properties of a material. Recent advances in the fields of computer vision and high-dimensional analysis have made computer-based shape characterization feasible on a variety of materials.
520 $a In this work, the relationship between microstructural shapes, and the properties and function of the material as a whole, is explored using moment invariants as global shape descriptors. A different relationship is examined in each of three material systems: how the three-dimensional shapes of cells in the cotyledons of the plant Arabidopsis Thaliana can be used to identify cell function; the two-dimensional shapes of additive manufacturing feedstock powder and the ability to distinguish between images of powders from different samples; and the two-dimensional shapes of gamma prime precipitates and their influence on the creep resistance of single crystal nickel-base superalloys.
520 $a In the case of Arabidopsis Thaliana cotyledon cells, three-dimensional Zernike and Cartesian moment invariants were used to quantify morphology, and combined with size and orientation information. These feature sets were then analyzed using unsupervised and supervised machine learning methods. Moderate success was found using unsupervised methods, indicating that natural delineations in the data correlate to cell roles to some degree. Using supervised methods, a success rate of 90% was possible, indicating that these features can be used to identify cell function.
520 $a The ability of two-dimensional Cartesian moment invariants to distinguish meaningful features in particles of additive manufacturing feedstock was tested by using these features to classify images of feedstock. Ultimately, simple histogram matching methods were unsuccessful, likely because they rely on the most common particles to draw conclusions. A bag-of-words method was used, which uses high-dimensional visualization and clustering techniques to classify individual particles by common features. Histograms of particle clusters are then used to represent each image. This method was far more successful, and a correct classification rate of up to 90% was found, and comparable rates were discovered using invariants which describe the shapes only broadly. This indicates that moment invariants are an effective measure of the morphologies of these types of particles, and can be used to classify powder shapes, which control many properties which are relevant to the additive manufacturing process.
520 $a In the case of the superalloys, it has been shown that the shape distribution of gamma prime precipitates can be tracked using second order moment invariants. In addition, several low-order moment invariants are shown to correlate to creep resistance in four alloys examined, which supports the idea that the shape of precipitates plays role in determining creep resistance in these alloys.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Materials science. $3 557839
655 7 $a Electronic books. $2 local $3 554714
690 $a 0794
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Carnegie Mellon University. $b Materials Science and Engineering. $3 1190340
773 0 $t Dissertation Abstracts International $g 79-09B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10822000 $z click for full text (PQDT)