Content Based X-ray Image Analysis of Aluminium Castings

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F11%3A86080839" target="_blank" >RIV/61989100:27360/11:86080839 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1504/IJCMSSE.2011.042820" target="_blank" >http://dx.doi.org/10.1504/IJCMSSE.2011.042820</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1504/IJCMSSE.2011.042820" target="_blank" >10.1504/IJCMSSE.2011.042820</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Content Based X-ray Image Analysis of Aluminium Castings

Popis výsledku v původním jazyce

The X-ray digital images of aluminium castings containing different microstructure defects were characterized by feature vectors composed from i) first-order statistics, ii) singular values and iii) second-order statistics calculated from Grey Level Co-occurrence Matrices (GLCM). The most suitable features were found by means of the Ward?s clustering method. The X-ray images characterised by the first-order statistics, such as 1st to 6th statistical moments and entropy, were portioned in 2 main clusterswith efficiency of 90 %. Consequently, using the 6 statistical moments and entropy, aluminium castings were sorted according to their quality in comparison with one casting of no observable defects. Their similarity was expressed measured by the Euclidean distance (ED). At ED = 5 the quality aluminium castings were effectively separated from the defective ones. This image analysis approach can be simply implemented into the automatic quality control of metallurgical processes and could

Název v anglickém jazyce

Content Based X-ray Image Analysis of Aluminium Castings

Popis výsledku anglicky

The X-ray digital images of aluminium castings containing different microstructure defects were characterized by feature vectors composed from i) first-order statistics, ii) singular values and iii) second-order statistics calculated from Grey Level Co-occurrence Matrices (GLCM). The most suitable features were found by means of the Ward?s clustering method. The X-ray images characterised by the first-order statistics, such as 1st to 6th statistical moments and entropy, were portioned in 2 main clusterswith efficiency of 90 %. Consequently, using the 6 statistical moments and entropy, aluminium castings were sorted according to their quality in comparison with one casting of no observable defects. Their similarity was expressed measured by the Euclidean distance (ED). At ED = 5 the quality aluminium castings were effectively separated from the defective ones. This image analysis approach can be simply implemented into the automatic quality control of metallurgical processes and could

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JG - Hutnictví, kovové materiály

OECD FORD obor

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Projekt

<a href="/cs/project/ED0040%2F01%2F01" target="_blank" >ED0040/01/01: Regionální materiálově technologické výzkumné centrum</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název periodika

International Journal Computational Materials Science and Surface Engineering

ISSN

1753-3465

e-ISSN

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Svazek periodika

4

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

13

Strana od-do

219-231

Kód UT WoS článku

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EID výsledku v databázi Scopus

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