A Multiobjective Evolutionary Multiscale Transformer Incorporating Fractal Features for Steel Materials Quality Analytics

The surface quality of steel materials is significantly influenced by processing conditions, which may result in roughness, flatness deviations, and various surface defects. However, the diversity of defect types and the limited size of labeled datasets pose challenges for accurate and efficient defect identification. To address these challenges, this paper proposes a multiobjective evolutionary multiscale Transformer incorporating fractal features for surface quality analytics of steel materials. Specifically, a multiscale Transformer is constructed, consisting of the convolutional tokenization architecture embedded with the multiscale attention module (MAM) and stacked Transformer encoders, enabling the model to effectively capture both morphological patterns and local defect details. In addition, a novel fractal dimension feature fusion module (FDFFM) is introduced to describe the irregularity of defect textures, enhancing feature representation. To achieve a balance between recognition accuracy and model complexity, a multiobjective evolutionary algorithm (MOEA) is employed, with the final model selected based on a knee point selection strategy to support decision-making. Experimental results validate the superior performance and efficiency of MOEA-FM-Trans compared to state-of-the-art methods.