Combinatorial Materials Science and EBSD: A High Throughput Experimentation Tool

Rajan, Krishna

doi:10.1007/978-0-387-88136-2_14

Cited by 2 publications

(1 citation statement)

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“…EBSD utilizes automated Kikuchi pattern indexing software to determine the orientation of multiple grains at once, making it a fast and accurate method for generating large quantities of data. Indexing of patterns involves the identification of individual (Kikuchi) bands based on the presence or absence of specific crystallographic poles (zone axes) as well as bandwidth [4,5]. It is important to define the notation of the extrudate axes to better describe the areas of interest for the EBSD analysis, which is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Electron backscatter diffraction (EBSD) as a tool for analysis of metal flow in aluminum extrusion

Pires

2023

Materials Research Proceedings

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Abstract. Electron backscatter diffraction was used to analyze the crystallographic texture within extruded AA6005 hollow profiles, allowing for the analysis of metal flow through various extrusion porthole die designs of different metal deformation conditions. Control of metal flow through a die design is necessary to meet industry demands for faster-running dies, tighter tolerances, thinner walls, and reduced extrusion loads. A standard die was compared with an innovative die design that doubled the extrusion exit speed. Analysis of metal flow was performed to provide additional insight into the uniformity of the metal flow, especially near the extrusion welds. Finite element method simulations were performed to predict the die exit temperature and plastic strain distributions. Local texture varied across the core and surface regions of the tube wall but remained consistent between both extrudates. A peripheral coarse grain structure was found within both extrudates, due to their elevated exit die temperatures.

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Section: Introductionmentioning

confidence: 99%

Electron backscatter diffraction (EBSD) as a tool for analysis of metal flow in aluminum extrusion

Pires

2023

Materials Research Proceedings

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Harnessing Legacy Data to Educate Data-Enabled Structural Materials Engineers

2020

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Data-driven materials design informed by legacy data-sets can enable the education of a new workforce, promote openness of the scientific process in the community, and advance our physical understanding of complex material systems. The performance of structural materials, which are controlled by competing factors of composition, grain size, particle size/distribution, residual strain, cannot be modelled with single-mechanism physics. The design of optimal processing route must account for the coupled nature of the creation of such factors, and requires students to learn machine learning and statistical modelling principles not taught in the conventional undergraduate or graduate level Materials Science and Engineering curricula. Therefore, modified curricula with opportunities for experiential learning are paramount for workforce development. Projects with real-world data provide an opportunity for students to establish fluency in the iterative steps needed to solve relevant scientific and engineering process design questions.

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Combinatorial Materials Science and EBSD: A High Throughput Experimentation Tool

Cited by 2 publications

References 48 publications

Electron backscatter diffraction (EBSD) as a tool for analysis of metal flow in aluminum extrusion

Electron backscatter diffraction (EBSD) as a tool for analysis of metal flow in aluminum extrusion

Harnessing Legacy Data to Educate Data-Enabled Structural Materials Engineers

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