Morphological assessment of cavitation caused damage of cordierite and zircon based materials using principal component analysis

The pattern recognition approach, explored by this study, applies the principal component analysis (PCA) as the most widely used statistical method with the aim of assessing the initiation and propagation of the cracks and defects that appear on the surface of material exposed to the cavitation. The experiment was performed in four stages: (a) synthesis of two ceramic materials (cordierite and zircon); (b) subjecting the samples to the cavitation; (c) using image analysis software for collecting the data about morphological characteristics that describe defects; (d) principal component analysis as a pattern recognition tool in order to characterize the defects at the material surface. Besides that, according to standard, cavitation erosion was monitored by determining material mass loss during the cavitation. Large experimental datasets collected from morphological descriptors by image analysis are multivariate and difficult to interpret, thus are processed by principal component analysis as the most informative technique for extracting possible differences. The performed approach proved that this method has a great potential for better assessment of induced defects by proper distinguishing among them at different levels and that can be considered a very efficient and cost-effective one.

Automated summary

The pattern recognition approach in this study utilized principal component analysis (PCA) to evaluate the initiation and propagation of cracks and defects on the surface of cavitated materials. The experiment involved synthesizing two ceramic materials, subjecting them to cavitation, collecting morphological data using image analysis software, and using PCA as a pattern recognition tool to characterize the defects. The approach proved to be highly efficient and cost-effective, offering a better assessment of induced defects and distinguishing them accurately at different levels.