Prediction of cross section morphology of Ni based cladding layer by laser cladding

BP neural network was used to prediction model of key parameters（laser power, scanning speed, powder feeding voltage, powder carrier gas flow rate） of laser cladding and cross section morphology（melting width and reinforcement height） of cladding layer. Taking the laser cladding parameters as the input and the cross-section morphology of the cladding layer as the output, the network was trained by the process test data to realize the high mapping between input and output. The results showed that BP neural network could better predict morphology of cladding layer and the prediction error of double hidden layer BP neural network model fluctuates was less, which showed excellent stability. Compared with single hidden layer neural network, the maximum prediction error of BP neural network was greatly reduced.