In this paper, the damping capacity and the structural influence of the hard coating on the given bladed disk are optimized by the non-dominated sorting genetic algorithm (NSGA-II) coupled with the Kriging surrogate model. Material and geometric parameters of the hard coating are taken as the design variables, and the loss factors, frequency variations and weight gain are considered as the objective functions. Results of the bi-objective optimization are obtained as curved line of Pareto front, and results of the triple-objective optimization are obtained as Pareto front surface with an obvious frontier. The results can give guidance to the designer, which can help to achieve more superior performance of hard coating in engineering application.
ShaoFYangKZhaoHet al.Effects of inorganic sealant and brief heat treatments on corrosion behavior of plasma sprayed Cr2O3-Al2O3 composite ceramic coatings. Surface Coat Technol2015; 276: 8–15.
3.
Easterday OT. An experimental characterization of damping properties of thermal barrier coatings at elevated temperatures. Dissertations & Theses, Gradworks, 2011.
4.
Torvik PJ and Langley B. Material properties of hard coatings developed for high damping. In: 51st AIAA/SAE/ASEE joint propulsion conference, Orlando, FL, 27–29 July 2015.
5.
SunWHanQQiF. Optimal design of damping capacity for hard-coating thin plate. Adv Vibrat Eng2013; 12: 179–192.
6.
YenHY. New analysis and design procedures for ensuring gas turbine blades and adhesive bonded joints structural integrity and durability. Doctoral dissertation, The Ohio State University, 2000.
7.
ChenYZhaiJHanQ. Vibration and damping analysis of the bladed disk with damping hard coating on blades. Aerosp Sci Technol2016; 58: 248–257.
8.
BazARoJ. Optimum design and control of active constrained layer damping. J Vibrat Acoust1995; 117: 135–144.
9.
AraújoALSoaresCMMSoaresCAM. Optimal design of active, passive, and hybrid sandwich structures. Proc SPIE2008; 6926: 69260T1–69260T12.
10.
MadeiraJFAAraújoALSoaresCMMet al.Multiobjective optimization of viscoelastic laminated sandwich structures using the Direct MultiSearch method. Comput Struct2015; 147: 229–235.
11.
QinXWangYLuCet al.Structural acoustics analysis and optimization of an enclosed box-damped structure based on response surface methodology. Mater Des2016; 103: 236–243.
12.
ZaiweiLLiangX. Vibro-acoustic analysis and optimization of damping structure with response surface method. Mater Des2007; 28: 1999–2007.
13.
SacksJWelchWJMitchellTJet al.Design and analysis of computer experiments. Stat Sci1989; 4: 409–435.
14.
SimpsonTWMaueryTMKorteJJet al.Kriging models for global approximation in simulation-based multidisciplinary design optimization. AIAA J2012; 39: 2233–2241.
FanLXiangZXueMet al.Robust optimization of thermal-dynamic coupling systems using a Kriging model. J Spacecraft Rocket2015; 47: 1029–1037.
17.
DebK. A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Transact Evolution Computat2002; 6: 182–197.
18.
XuCLinSYangY. Optimal design of viscoelastic damping structures using layerwise finite element analysis and multi-objective genetic algorithm. Comput Struct2015; 157: 1–8.
19.
ZamanifarMFaniBGolshanMEHet al.Dynamic modeling and optimal control of DFIG wind energy systems using DFT and NSGA-II. Electric Power Syst Res2014; 108: 50–58.
20.
ChenSShiTWangDet al.Multi-objective optimization of the vehicle ride comfort based on Kriging approximate model and NSGA-II. J Mech Sci Technol2015; 29: 1007–1018.
21.
DebK. Multi-objective optimization using evolutionary algorithms, Chichester: John Wiley & Sons, 2001.
22.
LiCJOhmoriA. Relationships between the microstructure and properties of thermally sprayed deposits. J Therm Spray Technol2002; 11: 365–374.
23.
JuSShenoiRAJiangDet al.Multi-parameter optimization of lightweight composite triangular truss structure based on response surface methodology. Compos Struct2013; 97: 107–116.
24.
JohnsonCDKienholzDA. Finite element prediction of damping in structures with constrained viscoelastic layers. AIAA J2012; 20: 1284–1290.
25.
HeltonJCDavisFJ. Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab Eng Syst Safe2003; 81: 23–69.
