Use of Classification Trees and Rule-Based Methods to Predict Shapes of Nano-Aggregates of Reinforcement Fillers

While manufacturing composite materials, reinforcement fillers inevitable collide with each other and subsequently they congregate to aggregates with different shapes. The shape of these nanoparticles aggregates are of great significance for the mechanical material properties and in consequence, kno...

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Veröffentlicht in:Applied mechanics and materials 2015-10, Vol.799-800 (Mechanical and Electrical Technology VII), p.130-134
Hauptverfasser: Ibarretxe, J., Jimbert, Pello, Fernandez-Martinez, R., Iturrondobeitia, M., Guraya-Díez, T.
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container_end_page 134
container_issue Mechanical and Electrical Technology VII
container_start_page 130
container_title Applied mechanics and materials
container_volume 799-800
creator Ibarretxe, J.
Jimbert, Pello
Fernandez-Martinez, R.
Iturrondobeitia, M.
Guraya-Díez, T.
description While manufacturing composite materials, reinforcement fillers inevitable collide with each other and subsequently they congregate to aggregates with different shapes. The shape of these nanoparticles aggregates are of great significance for the mechanical material properties and in consequence, knowing the percentage of aggregates of each shape within of a specific group of shapes can give an idea of the final properties of the material. This work classifies aggregates, a new dataset of 5713 carbon black aggregates gathered based on transmission electron microscopy image processing, using several classification trees and rule-based methods. Based on these methods several models are built, trained and tested to perform the classification. And then, the most reliable and accurate model to classify aggregates is selected, obtaining a testing accuracy of the 74.57% according to their shape.
doi_str_mv 10.4028/www.scientific.net/AMM.799-800.130
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source Scientific.net Journals
subjects Aggregates
Classification
Fillers
Image processing
Nanoparticles
Nanostructure
Reinforcement
Trees
title Use of Classification Trees and Rule-Based Methods to Predict Shapes of Nano-Aggregates of Reinforcement Fillers
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