Ti/TiN/(Ti,N)-DLC/Ti-DLC/DLC coatings were deposited on 431 stainless steel using direct current magnetron sputtering technology under different bias voltages(0 V, −100 V, −200 V and −300 V). The microstructure and tribocorrosion performance of these DLC coatings in seawater was investigated. The results indicated that under the bias voltages, a denser and smoother surface of DLC coatings with a higher bonding strength between coatings and substrates was observed related to the increased incident kinetic energy of deposited ionized atoms. When the bias voltage was −200 V, the surface roughness reduced from 9.81 nm to 7.03 nm, and the bonding strength enhanced from 8.23 N to 8.86 N. What is more, the sp³ bond proportion and the disorder degree in DLC coatings both increased, which resulted in improved hardness and deformation resistance. However, when the bias voltage was −300 V, the increase of the amorphization was associated with a simultaneous rise in internal stress, which reduced the hardness and bond strength a little (8.72 N). DLC coatings can effectively improve the tribocorrosion properties of 431 stainless steel in seawater. When the voltage was −200 V, the average friction coefficient decreased from 0.35 to 0.07, with shallower wear traces and the wear loss of the DLC coating also being the smallest. The abrasive wear caused by metal oxides falling off the grinding ball, and the plastic deformation of the DLC coatings are the main wear forms. The high-density structure of DLC coatings under bias voltages can not only prevent the rapid expansion of cracks during deformation, but also provides a physical barrier to the erosion, which improves the corrosion and friction resistance in seawater. The optimization of bias voltage can improve the tribological performance of DLC coatings by regulating the carbon chain bond and microstructure. These results provide reference for DLC preparation and their potential engineering applications in stainless steel.

For shaft parts, 45 steel has been widely used due to its favorable mechanical properties and low cost. However, the relatively low wear resistance of 45 steel limits its application. In this work, high-entropy alloy of FeCrCoMnSi_x (x = 0, 0.3, 0.6, 0.9, 1) coatings were prepared on the surface of a 45 steel substrate using laser cladding technology to improve the wear performance of 45 steel. The effect of the Si element on the microstructure and tribological property of these coatings is investigated. The results show that the structure of FeCrCoMn coatings is mainly an FCC + HCP dual-phase solid solution, grown in equiaxial crystals. When a small amount of Si (x = 0.3) is added, the BCC phase is generated in the coating; meanwhile, the microstructure is transformed into the divorced eutectic character. When the content of Si is x = 0.6, the eutectic structure is promoted, and the microstructure is refined and becomes denser. When the content of Si increases to x = 0.9 and 1.0, the metal silicate phase containing Mn and Cr is formed due to the precipitation of supersaturated solid solution. At the same time, the microstructure is transformed into dendritic crystals due to the composition super-cooling effect by the excessive Si element, inducing serious element segregation. The hardness of FeCrCoMnSi_x high-entropy alloy coatings increases to 425.8 HV when the Si content is 0.6 under the synergistic effect of the solid-solution and dense eutectic structure. The friction and wear analysis shows that the friction and wear mechanisms of the coating are mainly abrasive wear and oxidative wear. The coefficient of friction and the wear rate of the FeCrCoMnSi_x high-entropy alloy coating decreases to 0.202 and 4.06 × 10⁻⁵ mm³/N·m, respectively, when the content of Si is 0.6 due to the dense microstructure and high hardness. The above studies prove that the presence of Si in the FeCrCoMnSi_0.6 high-entropy alloy coating induces a refined eutectic microstructure and improves the coating’s anti-wear properties by increasing hardness and decreasing the coefficient of friction.

This paper presents an AI-driven intelligent control system for precision spray applications that integrates real-time perception, neural network prediction, reinforcement learning optimization, and online Bayesian learning. A two-tier architecture is established: Tier 1 employs neural network-based feedback control for rapid pressure adjustment (0.1–0.3 MPa), while Tier 2 deploys an RL agent for optimal nozzle selection when pressure control is insufficient. The neural network model achieves high prediction accuracy with an RMSE of 3.2 $\mu$m on the test set. The RL agent demonstrates effective decision-making, attaining a 94.5% success rate in simulation and closed-loop experiments for maintaining target droplet size within $\pm$10 $\mu$m. Online Bayesian updates improve prediction accuracy by an average of 8.7% after every 100 field observations. Experimental validation demonstrates autonomous optimization, rapid convergence (3–5 cycles), and continuous learning without human retraining, offering a fully autonomous solution suitable for UAV deployment and precision agriculture.