Component's surface quality predictions by laser rapid forming based on artificial neural networks

A single-channel polyline scanning mathematical model is established, and the mathematical description of surface quality is analyzed theoretically. It is drew the conclusion that the angle degree and the scanning speed are the main important process parameters for part surface quality. The artificial neutral networks (ANN) model is established to predict part surface quality based on laser solid forming (LSF). The input of this model is the angle degree of corner joint and the scanning speed which is the most important factor in LSF process. The output of this model is the data of surface characterization which is the difference between corner joint height and layers' height. The ANN model could predict parts' surface quality data under different corner joint' angle degree conditions after trained by experimental data. The mean squared error (MSE) is less than 0.01 between prediction data and experimental data.