Advances in plasma spraying technology for coatings: from particle flight to splat formation

Plasma spraying technology plays a crucial role in coating preparation owing to its high efficiency and versatility. This technology heats and accelerates the spraying powder of metals, ceramics, polymers, and their composites through ultra-high temperature and high-speed jets and then sprays them onto the substrate surface to form high-performance coatings. However, the quality of the coating is influenced by various factors, such as particle flight behavior, process parameters, and substrate conditions, and controlling its microstructure remains challenging. This paper aims to systematically elucidate the correlation mechanisms between particle flight dynamics, spreading kinetics, and coating performance, providing a theoretical basis for achieving precise control of the coating microstructure. Research shows that particles undergo critical stages such as acceleration, heating, diffusion, and solidification during flight, and their behavior directly affects the final performance of the coating. Through particle diagnostic techniques and multi-physics field numerical simulations, the influence of process parameters, such as the Ar/H2 flow rate, spraying power, and spraying distance, on particle temperature, velocity, and spreading morphology can be quantitatively analyzed. Furthermore, the regulatory effects of particle flight characteristics (such as Reynolds number and Weber number), post-collision diffusion behavior, and interface heat transfer mechanisms on the coating microstructure are revealed. The results indicate that precisely controlling the coupling of particle flight temperature and velocity is key to improving coating quality, and reasonable process parameters can effectively reduce defects such as pores and microcracks. In recent years, data-driven methods such as machine learning and deep transfer learning have shown potential in optimizing the spraying process. However, their practical industrial applications still have limitations. Current research mainly focuses on the particle behavior mechanism under ideal experimental conditions, while the influence of dust, vibration, and other interference factors in actual industrial environments has not been deeply explored. Moreover, online monitoring equipment incurs high costs. The operation is also complex, and the environmental adaptability is poor, making large-scale adaptation in industrial sites difficult. The interaction between different material systems (such as high-melting-point and low-electrical-conductivity ceramic materials) and process parameters also requires systematic research. To achieve the leap from laboratory to industrial sites for plasma spraying technology, it is necessary to continuously advance research in multiple directions: conduct more experiments and simulations close to actual working conditions to obtain reliable data and provide support for process optimization; develop real-time monitoring systems that adapt to harsh industrial environments to achieve dynamic integration of monitoring data and equipment control; systematically establish quantitative relationships between process parameters, material properties, and coating performance to guide targeted parameter design; and, finally, expand the application of machine learning in process autonomous optimization and intelligent coating design, promoting interdisciplinary cooperation and technological innovation. In summary, the development of plasma spraying technology requires the integration of knowledge from multiple disciplines, such as mechanical engineering, materials science, fluid mechanics, and data science. Through the dual promotion of basic research and engineering applications, coating performance can be improved, and the application scope of coatings can be expanded.

等离子喷涂（Plasma Spraying）技术凭借其高效性与多功能性，在涂层制备领域发挥着至关重要的作用。该技术借助超高温高速射流，将金属、陶瓷、聚合物及其复合材料的喷涂粉末加热并加速，随后喷涂至基体表面，形成高性能涂层。然而，涂层质量受颗粒飞行行为、工艺参数、基体状态等多种因素影响，且其微观结构的调控仍颇具挑战。本研究旨在系统阐明颗粒飞行动力学、铺展动力学与涂层性能之间的关联机制，为实现涂层微观结构的精准调控提供理论依据。研究表明，颗粒在飞行过程中会经历加速、加热、扩散、凝固等关键阶段，其行为直接影响涂层的最终性能。通过颗粒诊断技术与多物理场数值模拟，可以定量分析Ar/H2流量、喷涂功率、喷涂距离等工艺参数对颗粒温度、速度及铺展形貌的影响。此外，研究还揭示了颗粒飞行特性（如雷诺数（Reynolds Number）、韦伯数（Weber Number））、碰撞后扩散行为以及界面传热机制对涂层微观结构的调控作用。研究结果显示，精准调控颗粒飞行温度与速度的耦合关系是提升涂层质量的关键，合理的工艺参数可有效减少孔隙、微裂纹等缺陷。近年来，机器学习（Machine Learning）、深度迁移学习（Deep Transfer Learning）等数据驱动方法在优化喷涂工艺方面展现出应用潜力，但在实际工业应用中仍存在诸多局限。当前研究主要聚焦于理想实验条件下的颗粒行为机制，而实际工业环境中粉尘、振动等干扰因素的影响尚未得到深入探究。此外，在线监测设备成本高昂、操作复杂且环境适应性差，难以在工业现场大规模部署。不同材料体系（如高熔点低导电陶瓷材料）与工艺参数之间的交互作用也有待系统研究。为实现等离子喷涂技术从实验室到工业现场的跨越，需在多个方向持续推进研究：开展更多贴近实际工况的实验与模拟工作，获取可靠数据以支撑工艺优化；开发适配恶劣工业环境的实时监测系统，实现监测数据与设备控制的动态集成；系统构建工艺参数、材料性能与涂层性能之间的定量关系，以指导针对性的参数设计；最后，拓展机器学习在工艺自主优化与智能涂层设计中的应用，推动跨学科合作与技术创新。综上，等离子喷涂技术的发展需要融合机械工程、材料科学、流体力学、数据科学等多学科知识。通过基础研究与工程应用的双重推动，可进一步提升涂层性能，拓展涂层的应用范围。