Individual yarn fibre extraction from micro-CT: multilevel machine learning approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24410%2F21%3A00008514" target="_blank" >RIV/46747885:24410/21:00008514 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.tandfonline.com/doi/full/10.1080/00405000.2020.1865503" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/00405000.2020.1865503</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/00405000.2020.1865503" target="_blank" >10.1080/00405000.2020.1865503</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Individual yarn fibre extraction from micro-CT: multilevel machine learning approach

Popis výsledku v původním jazyce

The internal structure and mechanics of the fibre materials, such as yarn or woven textile, are highly complex. Exploring the fibre structure is an essential step in material engineering either from the experimental or computational point of view. In this study, a new method to extract geometrical and morphological parameters of fibre structures is proposed. The method benefits from standard image analysis and machine learning technique to efficiently extract fibre segments from microcomputer tomography data. The proposed algorithm is tested on the yarn and woven textile materials with different resolution and quality. The developed method can extract the individual fibres with varying accuracy from 73% to 100% with processing time 2–5 s on the tested samples.

Název v anglickém jazyce

Individual yarn fibre extraction from micro-CT: multilevel machine learning approach

Popis výsledku anglicky

The internal structure and mechanics of the fibre materials, such as yarn or woven textile, are highly complex. Exploring the fibre structure is an essential step in material engineering either from the experimental or computational point of view. In this study, a new method to extract geometrical and morphological parameters of fibre structures is proposed. The method benefits from standard image analysis and machine learning technique to efficiently extract fibre segments from microcomputer tomography data. The proposed algorithm is tested on the yarn and woven textile materials with different resolution and quality. The developed method can extract the individual fibres with varying accuracy from 73% to 100% with processing time 2–5 s on the tested samples.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20503 - Textiles; including synthetic dyes, colours, fibres (nanoscale materials to be 2.10; biomaterials to be 2.9)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of the Textile Institute

ISSN

0040-5000

e-ISSN

—

Svazek periodika

112

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

7

Strana od-do

1979-1985

Kód UT WoS článku

000604370600001

EID výsledku v databázi Scopus

2-s2.0-85098660717