To address the challenge of determining process parameters and predicting quality in the robotic spraying operation of gas turbine blades, a multi-pass and multi-parameter coating thickness distribution model is proposed, based on spraying experiments. Initially, a single-pass multi-parameter coating thickness distribution model is developed from flat plate spraying experiments. Using the basic elliptic double-β curve model, the process parameters and environmental humidity, which significantly influence the spraying process, are incorporated. The model function is then constructed and parameter fitting is performed based on the experimental data. Subsequently, the paint film overlap in the spraying process of gas turbine blades is analyzed. A segmented multi-pass multi-parameter coating thickness distribution model is proposed for multi-pass spraying with overlap distance. The reliability of this distribution model is validated through spraying experiments on 9-stage gas turbine blades. The proposed distribution model enables a fully automated coating thickness generation program for the robotic spraying process on gas turbine blades, providing essential support for the enhancement of the robotic spraying process.
SivakumarRMordikeBL. High temperature coatings for gas turbine blades: a review. Surf Coat Technol1989; 37(2): 139–160.
2.
RajendranR. Gas turbine coatings–an overview. Eng Fail Anal2012; 26: 355–369.
3.
PellerinCBookerSM. Reflections on hexavalent chromium: health hazards of an industrial heavyweight. Environ Health Perspect2000; 108(9): A402–A407.
4.
ArraisRCostaCMRibeiroP, et al. On the development of a collaborative robotic system for industrial coating cells. Int J Adv Manuf Technol2021; 115: 853–871.
5.
ArikanMASBalkanT. Process simulation and paint thickness measurement for robotic spray painting. CIRP Annals2001; 50(1): 291–294.
6.
BalkanTSahir ArikanMA. Modeling of paint flow rate flux for circular paint sprays by using experimental paint thickness distribution. Mech Res Commun1999; 26(5): 609–617.
7.
GadowRCandelAFloristánM. Optimized robot trajectory generation for thermal spraying operations and high quality coatings on free-form surfaces. Surf Coat Technol2010; 205(4): 1074–1079.
8.
ZhaoBFengHCaoL, et al. Modeling of the glaze layer thickness distribution formed by the air spray oriented to glaze spray process parameters. In: Proceedings of the 3rd international conference on mechanical engineering and intelligent systems, 2015, pp.345–350.
9.
XieXWangY. Research on distribution properties of coating film thickness from air spraying gun-based on numerical simulation. Eng Mater Sci2019; 9(11): 721.
10.
ZhangPGaoWZhaoC, et al. A parametric distribution model of electrostatic spray rotating bell and application for automobile painting. J Coat Technol Res2023; 20(5): 1727–1745.
11.
ChenYChenWLiB, et al. Paint thickness simulation for painting robot trajectory planning: a review. Ind Rob2017; 44(5): 629–638.
12.
BalkanTArikanM. Surface and process modeling and off-line programming for robotic spray painting of curved surfaces. In: International design engineering technical conferences and computers and information in engineering conference, 1999, vol. 1, pp.455–465.
13.
DiaoXDZengSXTamVWY. Development of an optimal trajectory model for spray painting on a free surface. Comput Ind Eng2009; 57(1): 209–216.
14.
XiaWYuSLiaoX. Paint deposition pattern modeling and estimation for robotic air spray painting on free-form surface using the curvature circle method. Ind Robot Int J2010; 37(2): 202–213.
15.
LiCSunWJinJ, et al. Numerical simulation and experimental study of dip angle spray film thickness under elliptical double Gaussian sum model. Adv Mech Eng2024; 16(3): 16878132241234185.
16.
XieXWangY. Research on distribution properties of coating film thickness from air spraying gun-based on numerical simulation. Coatings2019; 9(11): 721.
17.
ZhangYHuangYMGaofengGAO, et al. New model for air spray gun of robotic spray-painting. J Mech Eng2006; 42(11): 226–233.
18.
AssadiHSchmidtTRichterH, et al. On parameter selection in cold spraying. J Therm Spray Technol2011; 20: 1161–1176.
19.
LiuYErdemirAMeletisEI. Influence of environmental parameters on the frictional behavior of DLC coatings. Surf Coat Technol1997; 94: 463–468.
20.
Broniarz-PressLWłodarczakSMatuszakM, et al. The effect of orifice shape and the injection pressure on enhancement of the atomization process for pressure-swirl atomizers. Crop Prot2016; 82: 65–74.
21.
SharmaRTrigwellSBirisAS, et al. Effect of ambient relative humidity and surface modification on the charge decay properties of polymer powders in powder coating. IEEE Trans Ind Appl2003; 39(1): 87–95.
22.
BoZFangFZhenhuaS, et al. 2015, July. Fast and templatable path planning of spray painting robots for regular surfaces. In: 34th Chinese control conference (CCC), 2015, pp.5925–5930. IEEE.
23.
RenJSunYHuiJ, et al. Coating thickness optimization for a robotized thermal spray system. Robot Comput Manuf2023; 83: 102569.
24.
XieKQiuXCuiY, et al. Experimental study on the effect of spray cone angle on the characteristics of horizontal jet spray flame under sub-atmospheric pressure. Therm Sci2020; 24(5 Part A): 2941–2952.
25.
AlmansooriNAldulaijanSAlthaniS, et al. Manual spray painting process optimization using Taguchi robust design. Int J Qual Reliab Manag2021; 38(1): 46–67.
26.
ChenYChenWZChenK, et al. The influence of spraying angle on robotic trajectory planning. Appl Mech Mater2014; 442: 225–228.
27.
GoodmanEDHoppensteradtLTW. A method for accurate simulation of robotic spray application using empirical parameterization. In: Proceedings, IEEE International conference on robotics and automation, 1991, pp.1357–1368. IEEE Computer Society.
28.
DinizPSR. The least-mean-square (LMS) algorithm. In: Adaptive filtering: algorithms and practical implementation, 2020, Cham, Switzerland: Springer Nature, pp.61–102.
29.
CinivizMSalmanMSCanlıE, et al. Ceramic coating applications and research fields for internal combustion engines. Ceram Coat Eng2012; 6: 195–234.
30.
GibasABaszczukAJasiorskiM, et al. Low-pressure cold spraying of suspension TiO2 in a single pass–Process optimization. Surf Coat Technol2023; 472: 129933.
