Since laser cladding is a multivariable coupling process, the relation between clad bead and process parameters is non-linear. Moreover, it is very difficult to find out an exact analytic model to express this relation. In this study, a neural network model, which is often used for non-linear problems, is developed to explain the relationship between process variables and clad parameters. A normal backpropagation (BP) algorithm and an amended BP one are trained, and it is found that the amended BP algorithm has advantages over the normal BP one. The experimental results well agreed with those predicted by the neural network model, indicating that the developed BP neural network model can be used for prediction in applications.

Combination of back propagation(BP)neural network and particle swarm optimization(PSO) algorithms is used to optimize process variables during the laser cladding.BP neural network model is developed to express the relationship between the clad process variables and the clad parameters(the width,height of clad bead),and the samples obtained in experiments are used to train network model to form the perfect map relation between input and output.Then,PSO algorithm is used to grabble the suitable values of the process variables.The experimental clad parameters with the process variable values calculated by this optimization method are coincident well with the expected ones.It is verified experimentally that combination of BP neural network and PSO algorithms can help to obtain the expected laser clad quality.

Iron-based alloy coating was prepared by laser cladding on surface of nodular cast iron QT600-3.Microstructure,microhardness of the coating and distribution of element Fe and Cr at interface between the coating and substrate were examined by means of optical microscope,SEM,XRD and hardness test.The results show that the microstructure of the laser clad coating consisted of cellular crystal layer and dendrite layer is homogeneous and free of pore.The inter-diffusion of elements Fe and Cr in the coating and substrate near interface is detected,indicating that metallurgically bonding forms between the coating and the substrate.The hardness of the coating increases 2.6 times compared with that of substrate due to dispersion hardening of carbides in the coating.