Coatings

In recent years, steel surface defect detection has emerged as a significant area of focus within intelligent manufacturing research. Existing approaches often exhibit insufficient accuracy and limited generalization capability, constraining their practical implementation in industrial environments. To overcome these shortcomings, this study presents IMTS-YOLO, an enhanced detection model based on the YOLOv11n architecture, incorporating several technical innovations designed to improve detection performance. The proposed framework introduces four key enhancements. First, an Intelligent Guidance Mechanism (IGM) refines the feature extraction process to address semantic ambiguity and enhance cross-scenario adaptability, particularly for detecting complex defect patterns. Second, a multi-scale convolution module (MulBk) captures and integrates defect features across varying receptive fields, thereby improving the characterization of intricate surface textures. Third, a triple-head adaptive feature fusion (TASFF) structure enables more effective detection of irregularly shaped defects while maintaining computational efficiency. Finally, a specialized bounding box regression loss function (Shape-IoU) optimizes localization precision and training stability. The model achieved a 5.0% improvement in mAP50 and a 3.2% improvement in mAP50-95 on the NEU-DET dataset, while also achieving a 4.4% improvement in mAP50 and a 3.1% improvement in mAP50-95 in the cross-dataset GC10-DET validation. These results confirm the model’s practical value for real-time industrial defect inspection applications.

To evaluate the slip resistance of high-strength bolted friction-type connections subjected to different corrosion-protection treatments, calibration tests were performed on six representative faying-surface conditions: sand-blasted (uncoated), epoxy zinc-rich primer, waterborne inorganic zinc-rich coating, alcohol-soluble inorganic anti-corrosion anti-slip primer, a complete multi-layer protective coating system, and cold galvanizing. Fifteen test groups comprising 45 tensile specimens were examined to determine slip factors, which were then compared with values recommended in domestic and international design standards. The results show that sand-blasted surfaces (W type) exhibit stable slip factors of μ = 0.43–0.45; alcohol-soluble inorganic primer surfaces (S type) provide the highest slip resistance with μ = 0.49–0.51, representing an increase of approximately 13%–18% compared with sand-blasted surfaces; and cold-galvanized surfaces (D type) achieve favourable performance with μ ≈ 0.44. Waterborne inorganic zinc-rich surfaces (A type) yield μ ≈ 0.33, corresponding to a reduction of about 25%, and are suitable for non-slip-critical connections. In contrast, epoxy zinc-rich primers (C type) and complete multi-layer coating systems (X type) present lower slip factors of μ = 0.26–0.28 and μ ≈ 0.23, corresponding to reductions of approximately 35%–45% and about 50%, respectively, indicating that the X-type treatment is unsuitable for slip-critical applications. The influence of bolt diameter is limited, with slip-factor variations within 5%–8% under the same surface condition, and no statistically significant effect confirmed by two-way ANOVA. These findings provide a quantitative experimental basis for the design, classification, and future standardization of friction-type bolted connections with coated faying surfaces.

This state-of-the-art review provides a comprehensive, critical synthesis of the rapidly expanding field of HECCs, emphasizing the unique scientific challenges that distinguish these materials from conventional ceramics and high-entropy alloys. Key challenges of HECCs include accurately predicting stable phases and quantifying resultant material properties, optimizing complex fabrication and processing techniques, and establishing a robust correlation between the intricate microstructural characteristics and macroscopic performance. Unlike previous reviews that focus on individual ceramic families, this article integrates the novel features, diverse applications, and recent developmental breakthroughs across carbides, nitrides, borides, and oxides to reveal the unifying principles governing configurational disorder, phase stability, and microstructure property relationships in HECCs. A key novelty of this review work is the systematic mapping of fabrication pathways, including CTR, PAS, SPS, and reactive sintering, against the underlying thermodynamic and kinetic constraints specific to multicomponent ceramic systems. The review introduces emerging ideas such as HEDFT, machine-learning-assisted phase prediction, and entropy–enthalpy competition as foundational tools for next-generation HECC design and performance analysis. Additionally, it uniquely presents densification behavior, diffusion barriers, defect chemistry, and residual stress evolution with mechanical, thermal, and tribological performance across the coating classes. By consolidating theoretical intuitions with experimental developments, this article provides a novel roadmap for predictive compositional design, development, microstructural engineering, and targeted application of HECCs in extreme environments. This work aims to support researchers and coating industries toward the rational development of high-performance HECCs and establish a unified framework for future research in high-entropy ceramic technologies.