表面技术2026，Vol.55Issue(7)：252-263,12.DOI:10.16490/j.cnki.issn.1001-3660.2026.07.020
面向超音速火焰喷涂的高精度涂层厚度预测模型构建研究
Development of a High-accuracy Dynamic Model for Coating Thickness in High-velocity Oxygen-fuel(HVOF)Spraying
摘要
Abstract
This study presents a high-fidelity dynamic model developed to accurately predict coating thickness distribution in high-velocity oxygen-fuel(HVOF)thermal spraying,a process critically important for applying protective coatings onto complex components such as turbine blades and industrial valves.A significant limitation in current process planning is the inaccuracy of traditional geometric models,which typically rely on projecting a static,axisymmetric deposition pattern onto the workpiece surface.These models fail to account for the dynamic and interdependent variations of two paramount process parameters:the instantaneous spray distance and the gun-to-surface incidence angle.This oversight introduces substantial prediction inaccuracies on curved surfaces,ultimately hindering the achievement of uniform coating thickness and the transition towards robust digital manufacturing protocols.The primary contribution of this work is a novel multivariable parametric deposition model that dynamically integrates the coupled effects of spray distance and angle,thereby enabling a fundamental shift from simplistic projection-based methods towards a physics-informed simulation of the actual deposition process.The foundation of this model is a meticulously designed experimental campaign aimed at decoupling the intertwined effects of spray distance and angle.Single-track deposition experiments are conducted on flat substrates using a commercial WC-12Co powder feedstock.A Praxair JP-8000 HVOF system is employed,with fuel flow rates and powder feed rate maintain at constant and optimized levels.The experimental matrix constitutes a full factorial design,with spray distances systematically varying from 150 millimeters to 350 millimeters in increments of 50 millimeters,and gun incidence angles varying from 0 degrees to 60 degrees in 15-degree increments.The three-dimensional topography of each resultant coating bead is captured with high precision using a white-light optical profilometer,generating a comprehensive dataset of deposition profiles under varied geometrical conditions.Analysis of this dataset yields a critical insight:the fundamental deposition footprint is not a fixed entity but a shape-changing function.Key profile characteristics,including the peak deposition rate,the longitudinal and transverse distribution widths,and the degree of profile asymmetry,are all determined to be complex,non-linear functions of both spray distance and angle simultaneously.This finding invalidates modeling approaches that treat these parameters'effects as separable or additive.In light of this,a new multivariable deposition model is formulated.The model architecture centers on a dual Gaussian function,which provides a flexible basis for representing asymmetric deposition patterns.Its core innovation lies in parameterizing this Gaussian footprint:the four defining parameters are not constants but are instead expressed as distinct second-order response surfaces,where each parameter is a function of the instantaneous spray distance and angle.The coefficients for these response surface equations are uniquely determined through multivariate regression analysis of the complete experimental dataset.Consequently,the model's deposition footprint dynamically adapts its shape at every point along a robotic toolpath based on the real-time local geometry.For final thickness prediction on a complex part,the total coating buildup at any surface point is computed by spatially integrating the contributions from this continuously adapting footprint along the entire spray trajectory.Model validation is performed at two levels.Under static and single-track conditions,the model demonstrated exceptional fidelity,achieving a coefficient of determination exceeding 0.98 when fitting the measured bead profiles across the entire range of tested distances and angles.The most significant evaluation involves dynamic spraying trials on rotating spherical test specimens designed to mimic industrial ball valves of varying diameters.Compared with thickness maps obtained via coordinate measurement machining,the predictions from the proposed model show a mean relative error of less than fifteen percent.In a direct comparative assessment,a conventional normal projection model utilizing a fixed Gaussian distribution yields a mean relative error of approximately thirty-seven percent for the same components.This represents a sixty percent reduction in prediction error attributed to the new model.The detailed spatial analysis of the error distribution further confirms that the conventional model produces consistent and systematic biases,over-predicting thickness on surfaces oriented away from the gun and under-predicting on surfaces facing it.The proposed model successfully mitigates this systematic bias by accurately capturing the coupled distance-angle effect.In conclusion,this research delivers a significant advancement in the simulation and prediction of thermal spray processes.The developed model moves beyond the limitations of static projection by introducing a dynamic and coupled-response framework that accurately reflects the physical deposition behavior on complex geometries.It provides a powerful and practical tool for offline robot path programming and process optimization,enabling"first-time-right"coating applications on critical components.Furthermore,the generalizable response surface methodology establishes a foundation for future model extensions,such as the inclusion of additional process variables like traverse speed,or its adaptation to other thermal spray or directed energy deposition techniques,paving the way for more integrated and intelligent digital manufacturing solutions.关键词
超音速火焰喷涂/涂层厚度预测/多变量沉积模型/几何投影修正模型/高斯函数Key words
high-velocity oxygen-fuel spraying/coating thickness prediction/multivariable deposition model/geometric projection correction model/Gaussian function分类
矿业与冶金引用本文复制引用
黄鸿涛,余德平,么一盟,汤卿,李玉玺,苏军..面向超音速火焰喷涂的高精度涂层厚度预测模型构建研究[J].表面技术,2026,55(7):252-263,12.基金项目
四川省科技计划资助项目(2024ZDZX0015) （2024ZDZX0015）
四川大学自贡市校地科技合作专项资金项目(2025CDZG-10) Sichuan Science and Technology Program(2024ZDZX0015) （2025CDZG-10）
Sichuan University-Zigong City University-Local S&T Cooperation Special Fund Project(2025CDZG-10) （2025CDZG-10）
