Multi-objective simultaneous prediction of waterborne coating properties

Multi-objective simultaneous prediction of waterborne coating properties was studied by the neural network combined with programming. The conditions of network with one input layer, three hidden layers and one output layer were confirmed. The monomers mass of BA, MMA, St and pigments mass of TiO₂ and CaCO₃ were used as input data. Four properties, which were hardness, adhesion, impact resistance and reflectivity, were used as network output. After discussing the hidden layer neurons, learn rate and the number of hidden layers, the best net parameters were confirmed. The results of experiment show that multi-hidden layers was advantageous to improve the accuracy of multi-objective simultaneous prediction. 36 kinds of coating formulations were used as the training subset and 9 acrylate waterborne coatings were used as testing subset in order to predict the performance. The forecast error of hardness was 8.02% and reflectivity was 0.16%. Both forecast accuracy of adhesion and impact resistance were 100%.