Visual and Cognitive Analysis of Multivariate Data for Characterizing Al/Sic Metal Matrix Composites

Abstract

The work shows the results of the literature review of the methods for obtaining aluminiumsilicon carbide - metal matrix composites (Al/ SiC MMCs). This work also includes the collection, analysis, and systemization of the literature data where textual information is presented as a single lexical and semantic system and where numeral information is presented as a dimensional system. The analysis of the literature data was conducted by visual and cognitive modelling, so that methods of forming Al/SiC MMCs and operating parameters that provide the best properties of the material (maximum level of thermal conductivity and minimum level of thermal linear expansion) are determined. Compared to the literature data, the data are presented that were received in a series of tests for obtaining Al/SiC MMCs with spark plasma sintering from SiC, which was synthesized in atmospheric electric arc plasma. Within the framework of the given subject, the authors do not know any analogues of such an analysis and visualization system that allows us to analyse multivariate data, which is essential for solving issues of finding a correlation for the variety of initial parameters that characterize the process of obtaining Al/SiC MMCs and that characterize the cluster of properties for the obtained material. The comparison data are given for thermal conductivity levels of modern (aluminium) LED light devices and Al/SiC MMC samples.