Process parameters optimization of nanostructured ZrO₂-7%Y₂O₃ coating deposited by plasma spraying based on genetic algorithms and neural networks

BP neural networks and genetic algorithms were combined to optimize process parameters of the nanostructured ZrO₂-7% Y₂O₃ coating prepared by plasma spraying technique. The neural networks were trained based on the experimental results of orthogonal tests,and the BP neural networks model was developed to describe the relationship between coating properties (bonding strength and microhardness) and four main process parameters,including spraying distance,spraying electric current,primary gas pressure and secondary gas pressure. Meanwhile,the bonding strength and microhardness of the nanostructured coating were optimized with single-objective and multiobjective optimization methods based on the genetic algorithms. The results show that the prediction data agrees well with the experimental values,which indicates that the proposed model is correct and reliable. The maximum bonding strength and microhardness of the coating are 44 MPa and 1 266 HV,respectively. The overall performance of the coating is best when the spraying distance is 90.66 mm,spraying electric current 934.63 A,primary gas pressure 0.302 MPa and secondary gas pressure 0.892 MPa while keeping the weight of bonding strength and microhardness constant.