Metals

Central segregation, a typical internal defect in continuous casting slabs, significantly deteriorates the mechanical properties of steel products. However, traditional manual defect evaluation methods rely heavily on experience, are highly subjective and inefficient, making it difficult to meet the quality assessment requirements of today’s high-end steel materials. In this study, an approach which combines an unsupervised image enhancement algorithm and Otsu algorithm analysis was proposed to achieve automatic recognition and quantitative features extracting of central segregation in continuous casting slabs. The challenges posed by insufficient brightness and low contrast in central segregation images were addressed using unsupervised image enhancement algorithms. Following this enhancement, batch objective quantification of the segregation images was conducted through Otsu processing. Comparative experimental results showed that the enhanced images yielded an average Dice Similarity Coefficient of 0.965 for segregation recognition, representing a 38% improvement over unprocessed images, with consistent accuracy gains across complex segregation scenarios. This intelligent detection method eliminates the need for manually labeling a training set, substantially improves the consistency of segregation quantification and reduces the time cost significantly. Consequently, multiple parameters can be employed to quantify segregation characteristics, offering a more comprehensive and precise approach than current simplified rating methods. This advancement holds promise for enhancing quality control in steel processing and advancing Artificial Intelligence-driven technological progress within the metallurgical sector.

Simplex-centroid mixture design (SCMD) is applied to change the combination of Na₂SiO₃, KF, NaOH and NaAlO₂ to examine the influences of electrolyte components and their interactions on the thickness and corrosion resistance of micro-arc oxidation (MAO) coating of AZ91D magnesium alloy. The results indicate that the obtained regression equations are very significant (p-value < 0.01) and have high prediction accuracy (R² = 0.9893, 0.9989). Pareto analysis shows that the interactions effect between Na₂SiO₃, KF and NaAlO₂ on the coating thickness and corrosion resistance are 70.03% and 92.35%, respectively, which quantitatively confirms that there are interactions among electrolytes. The analysis of response surface methodology (RSM) demonstrates that the optimum formula is high concentration of Na₂SiO₃, high concentration of KF and low concentration of NaAlO₂. When Na₂SiO₃ is compounded with NaAlO₂, the two will react to form aluminosilicate colloids, resulting in increased viscosity of the electrolyte, and the coating corrosion resistance is poor. When the main salt of electrolyte is single Na₂SiO₃ or NaAlO₂, the corrosion resistance is better. KF can significantly improve the coating thickness and corrosion resistance. Pearson correlation coefficient (PCC) reveals that there is a remarkable relationship between thickness and the corrosion resistance in acidic media (r = 0.88927), which was determined by the corrosion mechanism of the latter.

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