Grain sizing of anodized aluminum by color image analysis

Grain size characterization of aluminum alloys can be correlated with thermomechanical processing properties [1]. To predict the processing characteristics of these alloys under certain combinations of strain, deformation, and temperature, the metallographic measure of the grain size can be used. Most of the techniques that have been proposed so far do not provide reliable and reproducible quantitative metallographic measurements of the grain size, due to human error [2,3]. Considering that this manual task is also tedious to perform, a general color image analysis algorithm is proposed to automate the characterization process using an optical microscope with polarized light. This algorithm was tested on several ingots and on rolled-aluminum samples. The results show robustness in several conditions, even when the grains can barely be seen by a human operator. Time constraints specific to industrial settings were taken into account when implementing the algorithm. A complete characterization of the entire surface of an ingot can be obtained within a reasonable time limit. The same algorithm was tested with other types of color material. The proposed framework for handling segmentation of color grains fulfills industrial requirements for the characterization of materials seen under a microscope with polarized light.