Image Recognition, Identification and Classification Algorithms in Cast Alloys Microstructure Analysis

Automatic identification and classification of objects being the results of image recognition algorithms became more and more popular in many aspects of human activity. On the other hand, manual stereological methods and conventional image analyzer are more often than not difficult and time-consuming tool to obtain the informative data especially for complicated microstructures. To solve these problems, a computer assisted quantitative metallographic analysis was explored. The input data for the proposed analysis was a set of digital 2D images of metal microstructures of the technical aluminum cast Al-Si alloys. Images were obtained by high quality cameras embedded in optical microscopes. The objects of interest were the precipitates of intermetallic phases of various morphological shapes. Traditionally, descriptions of microstructures have been based on measurements of topological relationships between the three-dimensonal space and two-dimensional microsections, such as grain size, the average volume of particles, volume fraction, size of particles in unit volume, etc. We consider these features to be insufficient for the process of classification which permits differentiation. Therefore, the computational methods of pattern recognition have been applied to both the statistical particle shape analysis and topological characterization of dendritic structures. Several examples of designed and implemented algorithms, including the measurements of compactness, scale and rotation invariant moments, fractal dimension, convex hull, lacunarity and many other parameters are presented. The key to this quantitative analysis is the manner of interpretation of aluminum alloys' planar microsections. It provides practical techniques for extracting quantitative information from measurements. It is these features that determine the mechanical properties, and any advanced understanding of microstructure property relations requires their quantitative description. \ The presented approach is aimed at designing a system for identification and classification of microstructures occurring in multiphase cast alloys. Image data representing diverse samples was taken into investigation. Within each sample alloys' features were determined based on a cast modeling process. Due to the fact that the presence of specific microstructures determines mechanical properties of cast alloys, an automated image based classification system may be an invaluable tool for developers of modern casting technology.