Application of Artificial Neural Networks to Optimize Processing - Properties of Ni-Tic Composite Coatings

The plating parameters for optimizing the wear and corrosion resistance of Ni-TiC composite coatings were selected by orthogonal test, mainly including the TiC particles concentration, current density, duty cycle, frequency and stirring rate. A three-layer BP (Back Propagation) neural network with Lavenberg-Marquardt algorithm was established by MATLAB, which was used to train the network and predicted orthogonal experimental data. In addition, the best parameters combination of the composite coating were predicted and verified by experiments. The results predicted through the proposed BP model are in good agreement with the experimental values, the relative error is small, and the maximum error is less than 3% and the coefficient of determination value is 0.9997.