Coatings

Cr-Al composite coatings were fabricated on Ti-6Al-4V alloy substrates via mechanical alloying using a high-energy planetary ball mill. The coatings exhibited a distinctive bilayer architecture comprising an inner layer with coarse reinforcing particles and an outer layer featuring a refined, homogenized microstructure. Systematic investigations were conducted to elucidate the influence of rotational speed on coating formation, microstructural evolution, phase composition, and high-temperature oxidation performance. The findings revealed that insufficient milling speeds failed to facilitate adequate powder deposition, resulting in poor interfacial adhesion and the formation of porous or thin coatings. Conversely, excessive rotational speeds induced surface roughening and coating delamination. Optimization studies identified 250 r/min as the optimal milling speed, yielding dense, well-adherent coatings with superior oxidation resistance. Cyclic oxidation testing at 850 °C demonstrated that coated specimens exhibited significantly reduced mass gain compared to uncoated substrates. Post-oxidation characterization confirmed the formation of a protective corundum-type oxide scale (α-Al₂O₃ and Cr₂O₃) and revealed a four-layered structure in the oxidized coating: (I) a dense oxide film serving as an oxygen barrier, (II) a dense alloyed layer, (III) a porous alloyed layer, and (IV) an inner diffusion zone. These results demonstrate that the mechanically alloyed Cr-Al coatings provide effective protection against high-temperature oxidation for Ti-6Al-4V alloy substrates.

Accurate tool wear monitoring plays a decisive role in machining efficiency, product quality and reliability in modern manufacturing systems. Existing deep learning methods struggle to balance the high-frequency transient features and low-frequency evolution trends in tool wear signals, often losing key temporal evolution details when processing long-range degradation data. Therefore, this paper proposes an online prediction method of tool wear value that combines multi-scale convolution and dual-attention temporal features. This method extracts local mutation and trend features in wear signals through multi-scale convolution, captures wear evolution features through bidirectional cyclic network, and adaptively fuses local detail information and global trend through dual attention mechanism SWGC-DA to generate a multi-scale time series feature-driven prediction model. The ablation experiment based on the PHM2010 public data set verifies the effectiveness of the network structure design and demonstrates the model’s superior predictive ability. Experiments on the self-built TiAl alloy milling dataset achieved a stable prediction of R² up to 99.1%, with MAE and RMSE of 2.29 and 2.47, respectively. The results show that this method significantly improves the accuracy and robustness of wear prediction.

The current gradation design of asphalt mixtures relies solely on sieve passing rates of single-sized aggregates. The quantitative evaluation of aggregate gradation is a challenge, considering the combined action of the geometric characteristics, size and passing rates of the aggregates. Analyzing the multi-dimensional geometric synergistic characteristics of graded aggregate can help to quantify the gradation. The AIMS II system was used to systematically and quantitatively evaluate the shape, angularity and texture of parameter distribution of single-sized aggregates. The synergistic effect of composite geometric characteristics on the mesoscopic interface behaviors was analyzed, and then a calculation model of aggregate gradation characteristic was established based on the gray relational analysis method. The results show that the lithology and source of aggregates govern the geometric characteristics indices of single-sized aggregates, whereas particle size controls the extent to which these geometric characteristics contribute to skeleton stability and interface interactions. A higher proportion of large-sized coarse aggregates results in a greater composite angularity index and a more stable skeleton structure within the asphalt mixture. Texture characteristics and particle size distribution are integrated into a unified composite texture index. As this index increases, the lubrication effect of asphalt on the aggregate skeleton becomes more pronounced. The aggregate gradation characteristic index demonstrates strong discriminative capability for different gradations and exhibits a robust linear correlation with aggregate–asphalt interfacial interaction indices. This index demonstrates strong capability to quantitatively describe the synergistic mechanism of multi-dimensional geometric characteristics and gradation types of asphalt mixtures.