Annealing is a commonly used post-processing method for composite thin strips but suffers from drawbacks such as long processing time, high energy consumption, and susceptibility to oxidation. Replacing annealing with electric pulse treatment (EPT) can address these issues. In this study, a specially designed fixture was used to investigate the effects of pulsed current on the bonding strength of T2 copper (Cu)/304 stainless steel (SS) composite thin strips. The initial strip, with a 50% reduction rate, was prepared using a two-high mill, resulting in a Cu/SS composite strip with a thickness of 0.245 mm. Pulsed current treatment was applied with peak temperatures ranging from 350 °C to 600 °C. The results showed that EPT significantly improved the bonding strength. A pulsed current of 55 A resulted in the highest average peel strength of 10.66 ± 0.93 N/mm, with a maximum Fe content on the Cu side of 7.39 ± 0.84%, while a pulsed current of 65 A resulted in the highest Cu content on the SS side, reaching 57.54 ± 2.06%. This study demonstrates that EPT effectively controls the deformation behavior and interface state of composite strips, producing Cu/SS composite thin strips with high bonding strength.

Surface roughness, critically measured by the Arithmetical Mean Roughness (Ra), is a vital determinant of workpiece functional performance. Traditional contact-based measurement methods are inefficient and unsuitable for online inspection. While machine vision offers a promising alternative, existing approaches lack robustness, and pure deep learning models suffer from poor interpretability. Therefore, MTFE-Net is proposed, which is a novel deep learning framework for surface roughness classification. The key innovation of MTFE-Net lies in its effective integration of traditional texture feature analysis with deep learning within a dual-branch architecture. The MTFE (Multi-dimensional Texture Feature Extraction) branch innovatively combines a comprehensive suite of texture descriptors including Gray-Level Co-occurrence Matrix (GLCM), gray-level difference statistic, first-order statistic, Tamura texture features, wavelet transform, and Local Binary Pattern (LBP). This multi-scale, multi-perspective feature extraction strategy overcomes the limitations of methods that focus on only specific texture aspects. These texture features are then refined using Multi-Head Self-Attention (MHA) mechanism and Mamba model. Experiments on a dataset of Q235 steel surfaces show that MTFE-Net achieves state-of-the-art performance with 95.23% accuracy, 94.89% precision, 94.67% recall and 94.74% F1-score, significantly outperforming comparable models. The results validate that the fusion strategy effectively enhances accuracy and robustness, providing a powerful solution for industrial non-contact roughness inspection.