Multiproperty Microstructure and Property Design of Magnetic Materials

Li, Dongsheng; Rollett, Anthony D.; Vialle, Greg; Garmestani, Hamid

doi:10.1115/1.2870235

Cited by 7 publications

(2 citation statements)

References 22 publications

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“…Such mathematical descriptions are also of great value to materials scientists, since they can completely replace all the costly and prolonged experimental practices used to characterize the microstructure of materials ( Ref 2,3). In addition, representing the microstructure of materials by means of mathematical functions will lead to design of microstructures (Ref [4][5][6] and ultimately computational determination of properties of the material ( .…”

Section: Introductionmentioning

confidence: 99%

Derivation of Process Path Functions in Machining of Al Alloy 7075

Tabei

Shih

Garmestani

et al. 2015

J. of Materi Eng and Perform

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The evolution of micro-texture below the machined surface is computationally modeled and experimentally verified. The orientation distribution functions of the grains below the surface were represented in spectral form. The microstructure descriptor coefficients were derived, and their change with respect to the change in the cutting feed rate was computationally calculated and monitored. Micro-texture experimental observations conducted by electron back-scatter diffraction technique verify the modeling outputs. Continuation of changing the process parameter was done by finite element method, and the evolution in texture was investigated by computational modeling. The process path function which correlates microtexture evolution and cutting feed rate, was obtained by applying the principle of orientation conservation in the Euler space. As a result of the major finding of this work, i.e., derivation of process path functions, the evolution of texture as a function of the material feed rate is numerically determined without any need to texture modeling or finite element analyses.

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Section: Introductionmentioning

confidence: 99%

Derivation of Process Path Functions in Machining of Al Alloy 7075

Tabei

Shih

Garmestani

et al. 2015

J. of Materi Eng and Perform

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show abstract

“…These properties are strongly dependent on the detailed microscopic substructure of the material. 16 In particular, most metallic materials consist of a large number of microscopic crystals, or "grains," and accurate segmentation of these grains can substantially facilitate the analysis of material properties and shorten the design period for new metallic materials.…”

Section: Application To Materials Science Imagesmentioning

confidence: 99%

Image sequence segmentation combining global labeling and local relabeling and its application to materials science images

2012

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Accurately segmenting a series of 2D serial-sectioned images for multiple, contiguous 3D structures has important applications in medical image processing, video sequence analysis, and materials science image segmentation. While 2D structure topology is largely consistent across consecutive serial sections, it may vary locally because a 3D structure of interest may not span the entire 2D sequence. In this paper, we develop a new approach to address this challenging problem by considering both the global consistency and possible local inconsistency of the 2D structural topology. In this approach, we repeatedly propagate a 2D segmentation from one slice to another, and we formulate each step of this propagation as an optimal labeling problem that can be efficiently solved using the graph-cut algorithm. Specifically, we divide the optimal labeling into two steps: a global labeling that enforces topology consistency, and a local labeling that identifies possible topology inconsistency. We justify the effectiveness of the proposed approach by using it to segment a sequence of serial-section microscopic images of an alloy widely used in material sciences and compare its performance against several existing image segmentation methods.

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Review of Structure Representation and Reconstruction on Mesoscale and Microscale

2014

JOM

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Multiproperty Microstructure and Property Design of Magnetic Materials

Cited by 7 publications

References 22 publications

Derivation of Process Path Functions in Machining of Al Alloy 7075

Derivation of Process Path Functions in Machining of Al Alloy 7075

Image sequence segmentation combining global labeling and local relabeling and its application to materials science images

Review of Structure Representation and Reconstruction on Mesoscale and Microscale

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