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.