Examination of Clustering in Eutectic Microstrcture

• Autorzy: Bortnyik K. , Barkóczy P.

Identyfikatory

DOI

10.1515/amm-2017-0169

• Języki publikacji: EN

Abstrakty

EN

The eutectic microstructures are complex microstructures and a hard work to describe it with few numbers. The eutectics builds up eutectic cells. In the cells the phases are clustered. With the development of big databases the data mining also develops, and produces a lot of method to handling the large datasets, and earns information from the sets. One typical method is the clustering, which finds the groups in the datasets. In this article a partitioning and a hierarchical clustering is applied to eutectic structures to find the clusters. In the case of AlMn alloy the K-means algorithm work well, and find the eutectic cells. In the case of ductile cast iron the hierarchical clustering works better. With the combination of the partitioning and hierarchical clustering with the image transformation, an effective method is developed for clustering the objects in the microstructures.

Słowa kluczowe

EN

eutectic; microscopy; image analysis; data mining; cluster analysis

• Wydawca: Institute of Metallurgy and Materials Science of Polish Academy of Sciences ; Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences
• Czasopismo: Archives of Metallurgy and Materials
• Rocznik: 2017
• Tom: Vol. 62, iss. 2B
• Strony: 1155--1159
• Opis fizyczny: Bibliogr. 11 poz., rys., wzory

Twórcy

autor

Bortnyik K.

• University of Miskolc, Institute of Physical Metallurgy, Metal forming and Nanotechnology, H-3515 Miskolc Egyetemváros

autor

Barkóczy P.

• University of Miskolc, Institute of Physical Metallurgy, Metal forming and Nanotechnology, H-3515 Miskolc Egyetemváros Fux Co. H-3527 Miskolc Besenyői Str. 8.

Bibliografia

• [1] Gácsi Z., Sárközi G., Réti T., Kovács J., Csepeli Zs., Mertinger V., Sztereológia és képelemzés. Miskolc, MicroPress Nyomda, 2001.
• [2] R. Nunes et al, ASM Metals Handbook 02.
• [3] Dr. Köves Elemér, Alumínium kézikönyv, Műszaki Könyvkiadó, Bp., 1984.
• [4] J. Han, M. Kamber, Data Mining Concepts and Techniques 3rd edition. Waltham USA, Elsevier, 2012.
• [5] M. Anderberg, Cluster Analysis for Applications. Morgan Kaufmann Publishers, 1973.
• [6] Knowledge-Based Clustering, New Jersey, John Wiley & Sons, 2005.
• [7] T.H. Cormen, C.E. Leiserson, R.L. Rivest: Introduction to Algorithms 3rd edition, MIT 2009.
• [8] S. Wolfram, A new kind of science, Wolfram Media, 2002.
• [9] Cs. Póliska, Z. Gácsi, P. Barkóczy, The Effect of Melt Flow on the Dendrite Morphology Materials Science Forum 508, 169-174 (2006).
• [10] Sz. Gyöngyösi, P. Barkóczy, Scaling cellular automaton simulations of short-range diffusion processes Materials Science Forum 729, 150-155 (2013).
• [11] P. Barkóczy, Gyors szomszédság kereső algoritmus véletlen rácsú cella automatához In: Gácsi Zoltán, Barkóczy Péter, Sárközi Gábor (szerk.) Képfeldolgozók és Alakfelismerők IV. Konferenciája. NJSZT-KÉPAF. 308 p.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)