Research on Classification Discriminating Method of Copper Matrix Composites

doi:10.3969/j.issn.1000-1093.2015.05.023

Abstract

Abstract: Surface microtopography of copper matrix composite is partitioned by orthogonal method, and the subgraphs are selected as training set at random. The characteristics of graphite particles are extracted by selecting and designing a basic element model, and the statistical inference of distribution law is made. It is known that the diameter center, slope, fractal dimension and density of graphite particles obey normal distribution, and its long axis and minor axis submit to logarithmic normal distribution. The distribution law of graphite particles can be inferred to be consistent by the way of significance test. The factor analysis method is used to determine the primary indices which characterize the complex surface microtopography of copper matrix composites, and the support vector machine principle is used to classify and discriminate the copper matrix composites with different graphite contents. The results show that, through the analysis of surface morphology, the classification accuracy of copper matrix composites, of which and mass fraction of graphite is 16%, reaches 83.333%, the classification accuracy of the two kinds of copper matrix composites, of which the mass fractions of graphite are 10% and 20%, between them reaches 100%.

Key words: metallic material, copper matrix composite, basic element, classification, support vector machine

CLC Number:

O212
TF125

MA Run-bo， DU Jian-hua， XU Shi-meng. Research on Classification Discriminating Method of Copper Matrix Composites[J]. Acta Armamentarii, 2015, 36(5): 921-927.

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