目的 提出一种适应涡轮叶片复杂曲面的涂层厚度优化策略,以解决传统热障涂层喷涂路径规划中存在的厚度不均匀、局部过喷与欠喷等问题,实现涡轮叶片涂层厚度的精准控制和均匀分布。方法 基于喷涂过程中能量与物质的高斯分布特征,结合曲面的几何信息,构建了涂层厚度沉积模型,提出并建立了曲率驱动的涂层厚度优化算法。该算法通过分析叶片表面的局部曲率变化,自适应地调整喷涂路径参数,从而实现对不同几何特征区域的动态优化。结果 采用响应面分析方法,选取了不同的曲率半径、喷涂角度和喷涂工艺组合形成响应面数据,并在涡轮叶片复杂曲面上进行了仿真验证。仿真结果表明,所提出的方法能显著降低涂层厚度的波动性,有效抑制了局部区域的过喷与欠喷现象,经优化后的涂层厚度均匀性可达93.8%。结论 本研究提出的基于曲率驱动的涂层厚度优化方法,能够有效解决复杂自由曲面叶片热障涂层厚度不均匀的问题,提升了涂层整体性能与喷涂效率。该方法为涡轮叶片精准涂层控制提供了一种高效可靠的优化策略,并为智能化热喷涂机器人路径规划与智能工艺控制提供了重要的理论依据和实践指导。

The thickness uniformity of thermal barrier coatings (TBCs) plays a pivotal role in ensuring effective and comprehensive thermal protection of turbine blades throughout their operational life cycle. Uniform coating thickness is crucial because inconsistencies can result in localized overheating, premature material degradation, and reduced service life of turbine blades, ultimately compromising the operational reliability and efficiency of gas turbine engines. Traditional approaches for achieving thickness uniformity primarily depend on extensive trial-and-error experimental iterations. This reliance on empirical methods not only significantly elevates manufacturing costs, but also considerably reduces production efficiency and repeatability.
To overcome these inherent limitations, the work aims to introduce an innovative and systematic approach integrating precise geometric analysis of blade surfaces with advanced Gaussian distribution modeling of spray energy and material deposition dynamics. The core objective is to facilitate dynamic optimization of the coating spray process through predictive modeling rather than empirical experimentation. Specifically, a novel coating thickness deposition model is proposed, which uniquely incorporates the intricate local geometric characteristics inherent in complex turbine blade surfaces, thereby bridging the gap between geometric complexity and coating performance. Building upon this innovative geometric modeling framework, a curvature-driven optimization algorithm specifically tailored to dynamically adjust critical spray parameters is further developed. These parameters include spray angle, trajectory spacing, and spray distance, which are all strategically modulated based on precise local curvature metrics derived from detailed blade surface geometrical analyses. By continuously adjusting these parameters in real-time or near-real-time during the coating spray process, the algorithm ensures optimal thickness distribution even across highly intricate and geometrically complex regions of turbine blades. Such targeted adjustments effectively minimize typical coating defects such as over-spraying (excess deposition resulting in wastage and surface irregularities) and under-spraying (insufficient coating leading to inadequate thermal protection). To rigorously validate and assess the practical efficacy and robustness of the proposed optimization method, an extensive response surface analysis (RSA) was meticulously conducted. The RSA systematically combined various critical curvature radii, including values at 20 mm, 40 mm, 60 mm, and 200 mm, alongside distinct spray angles such as 45°, 60°, 75°, and 90°. This comprehensive set of parameter combinations facilitated the generation of a detailed, multidimensional response surface dataset. Subsequently, rigorous numerical simulations employing these systematically varied parameters were performed on realistically modeled turbine blade geometries. This enabled a comprehensive evaluation of the coating performance across a wide spectrum of curvature scenarios and spray angles. Results from these detailed numerical simulations demonstrated significant improvements in coating thickness uniformity. The optimized spraying paths, driven by curvature-sensitive adjustments, effectively mitigated previously challenging local variations, resulting in a remarkable uniformity achievement rate of approximately 93.8%. Beyond demonstrating clear quantitative improvements, detailed analyses of simulation data offered novel and critical insights into the underlying physical mechanisms by which surface geometry, specifically local curvature, affected spray energy distribution and subsequent deposition patterns. Crucially, the mechanisms were clarified through which curvature-driven parameter adjustments substantially reduced localized deposition errors, thereby significantly enhancing both coating uniformity and overall coating quality. Furthermore, clear and quantifiable correlations were established between optimal spray parameters obtained from RSA and specific curvature profiles, enabling predictive and adaptive control strategies essential for the implementation of intelligent robotic spraying systems. In conclusion, an original, curvature-driven methodology is significantly advanced for dynamically optimizing thermal barrier coating thickness on complex free-form turbine blade surfaces. This innovative approach not only markedly enhances coating uniformity and operational efficiency but also represents a substantial improvement over conventional empirical iterative methods. The proposed model and optimization strategy offer robust theoretical foundations and practical guidance crucial for intelligent robotic path planning and fully automated spray coating processes. Consequently, this study provides valuable insights into cutting-edge thermal spray technologies, paving the way for further advancements in smart manufacturing, precision process control, and enhanced operational reliability of turbine systems.