TITLE:
Quantitative Metallographic Analysis of Dual-Phase Microstructures in 5083 Aluminum Alloy: A Dual-Channel MATLAB Image-Processing System
AUTHORS:
Liuqiang Wei, Liang Yu, Xu Zheng, Qizhong Zhao, Yue Huang, Yanli Jiang
KEYWORDS:
5083 Aluminum Alloy, Quantitative Metallography, Marker-Controlled Watershed, Adaptive Thresholding, β-Phase (Al3Mg2), MATLAB, Semi-Automated Image Analysis
JOURNAL NAME:
World Journal of Engineering and Technology,
Vol.14 No.4,
October
19,
2026
ABSTRACT: Recrystallized grains and β-phase (Al3Mg2) precipitates coexist in annealed 5083 aluminum alloy with overlapping gray levels, which hinders automated quantitative metallography. A semi-automated dual-channel quantitative metallographic analysis system was developed on the MATLAB platform by decoupling preprocessing and segmentation according to target scale. In the grain channel, images are standardized by combined median-Gaussian filtering, contrast stretching and rolling-ball background correction, and then segmented by an improved marker-controlled watershed algorithm incorporating multi-scale gradient information. In the β-phase channel, conservative CLAHE (ClipLimit = 0.01) locally enhances the fine precipitates without losing their contours, and a strategy adaptive to the imaging mode (OM grayscale, OM color, SEM-BSE) selects block-wise Otsu, CIELAB color masking or foreground-polarity switching. Once the four case-specific inputs have been supplied by the operator (the scale-bar endpoints, the imaging mode, the segmentation sensitivity and the minimum particle area), the system automatically extracts grain size, ASTM grain-size number G, and β-phase area fraction, morphological and distribution parameters, and supports batch processing. Validation on 13 micrographs of different processing states shows that the mean absolute relative error of the total particle number between automatic and manually assisted counting is 1.48% (maximum 3.37%) with a Pearson correlation coefficient of 0.9953 and a systematic underestimation of about 0.9%; the segmented grain boundaries match the microstructures well, the system-computed G values agree with manual ratings, the segmented second-phase features are reported as contrast-defined features (predominantly β-Al3Mg2), because their identity was established from the imaging-mode-specific contrast rather than from point-by-point EDS/XRD confirmation. Per-image comparisons of total second-phase particle counts were conducted between the proposed system and manual counting. Numerical cross-validation of grain-scale parameters, including grain-size number, equivalent grain diameter and boundary overlap, is yet to be accomplished.