Overcoming optical image challenges in automatic grain size measurement using a novel computer vision algorithm applied to hot deformation of Al-Zn-Mg Powder metallurgy alloy

Abstract

The present study introduces a novel approach that utilizes the Python programming language along with the OpenCV, SciPy, and NumPy libraries. This approach addresses challenges related to grain size measurement in optical images, including issues such as fused grains caused by fragmented boundaries, noise resulting from lens aberrations, and grains located at the image edges. These challenges have been successfully overcome, enabling the segmentation of microstructures and the automated determination of grain characteristics, including grain counting, ASTM grain size, and grain size in µm. The developed algorithm was validated using optical images obtained after hot deformation, and a comparative analysis was performed with manual measurements. The results showed an average absolute relative error (AARE) of 3.1% for the number of grains, 0.19% for ASTM grain size, and 1.59% for grain size in µm, with an R-value of 0.999.