This work investigates the spatial distribution pattern of silicon carbide particles in an aluminum alloy (Al/SiC) using two-dimensional spectral spatial analysis. This technique is based on the Fourier transform of the autocovariance function sample. The spectral analysis is a powerful tool for investigating the spatial distribution of points, since it does not assume structural characteristics of the data before analysis and uses only the spectral function. The coordinates of the centers of the particles were acquired through image processing techniques. The polar periodogram spectrum and Monte-Carlo methods are used to test the hypothesis of complete spatial randomness of the particles. The results show both the silicon carbide particles have completely random spatial distribution in the aluminum alloy and the spatial spectral analysis can be an alternative tool to investigate point patterns in space.
polar spectrum; point processes; material properties; spatial distribution
