Using neural networks to predict bending angle of sheet metal formed by laser

In this paper, three supervised neural networks are used to estimate bending angles formed by a laser. Inputs to these neural networks are known forming parameters such as spot diameter, scan speed, laser power, and workpiece geometries including thickness and length of sheet metal workpiece. For comparison, regression models are also used to estimate bending angle. Verification experiments are then conducted to evaluate the performance of these models. It is shown that the radial basis function neural network model is superior to other models in predicting bending angle. The volume energy model is better than the line energy model in angle prediction.