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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
