Machine-Learning-Based Automatic Metallographic Grading System for High-Gloss Anodized Aluminum Profiles

The excellent “mirror” effect of medium and high-strength aluminum alloy profiles from the 6-series, achieved through anodizing, is highly valued by customers. Metallographic analysis is a key method for predicting the anodizing effect. However, traditional metallographic analysis methods suffer from unstable accuracy and low efficiency. To address these issues, this paper successfully develops a metallographic grading system by constructing a dataset and integrating computer vision with machine-learning techniques. Based on grain classification, the system automatically determines the metallographic grade by analyzing the proportion of good grain areas. After applying SMOTE sampling and 10-fold cross-validation to the machine-learning algorithm, we conducted a comparative analysis of the model’s performance from the perspectives of accuracy, good grain recall rate, bad grain recall rate, and AUC. The XGBoost model, selected as the final predictive model from 18 machine-learning models due to its superior performance, achieved a grain classification accuracy of 96.21% and a good grain recall rate of 98.07%. Both the accuracy and good grain recall standard deviations were less than 0.02. These results indicate that the model can effectively distinguish between good and bad grains with high robustness. Additionally, the average time for metallographic grading is less than 9 s. In comparison to the instability of traditional manual grading, this method significantly enhances both the accuracy and efficiency of metallographic analysis while also reducing grading costs.