Coatings

Surface inspection of aero-engine blades is critical for aero-engine production and maintenance. However, composite materials like titanium alloys and superalloys, as well as thermal barrier coatings on blades, exhibit distinct optical reflection properties, while their complex curved surfaces cause severe image reflections leading to overexposure, underexposure, edge blurring and reduced measurement accuracy. To solve this, we propose ELANet, a deep-learning-based multi-exposure image fusion method with DenseNet as the backbone. Its key innovations include two parts: first, an Efficient Channel Attention mechanism to capture reflection feature differences between substrate and coating, prioritizing resource allocation to anti-reflection channels; second, an Ultra-Lightweight Subspace Attention Mechanism with only one-fifth the parameters of traditional spatial attention that adaptively assigns weights to local features based on curved surface reflection laws, enhancing edge and detail extraction while reducing computational cost. The Efficient Channel Attention and Ultra-Lightweight Subspace Attention Mechanism synergistically address exposure and blurring issues. Validated against 12 mainstream methods via 9 quantitative metrics, ELANet achieves state-of-the-art performance: MEF-SSIM reaches 0.9472, which is 1.3% higher than the best comparative method, PSNR reaches 21.48 dB, which is 2.2 percent higher than the second-best method, and the average processing time is 0.48 s. Ablation experiments confirm the necessity of the Efficient Channel Attention and Ultra-Lightweight Subspace Attention Mechanism. This method effectively supports high-precision blade inspection.

Coarse grains and blocky M-A constituents are often generated in the heat-affected zone (HAZ) of fusion welded pipeline steel joints owing to high heat input, causing a significant deterioration of toughness. This study demonstrated the effect of heat input in friction stir welding (FSW) on the microstructure and toughness of the nugget zone (NZ), elucidating the microstructure evolution and toughening mechanism. The results revealed a marked reduction in effective grain size within the NZ at low heat input (LHI) and a significant increase in the ratio of the refined M-A constituent. Furthermore, the decreased heat input leads to weak texture components (D1, D2, and F) accompanied by a decrease in the kernel average misorientation (KAM) value. This microstructural optimization clearly enhances toughness, and an excellent toughness value of 200 J, representing 95.3% of the basal metal (BM), was achieved in the NZ at LHI. The primary reason for this improvement is the refinement of effective grains and M-A constituents resulting from reduced heat input. During crack propagation, the high proportion of effective grain boundaries and fine M-A constituents acts as a barrier, arresting and deflecting cracks and thereby enhancing toughness.

The journal retracts the article “New Imidazolium Ionic Liquids from Recycled Polyethylene Terephthalate Waste for Curing Epoxy Resins as Organic Coatings of Steel” [...]