Study on error correction in dual galvanometer scanning system based on elman recurrent neural network

Non-linear error of the dual galvanometer scanning system in the rapid prototyping machines directly influences the precision of the laser scanning system. A method based on neural network (NN) was proposed for correcting error in the system. Back propagation NN, radial basis function NN, and Elman recurrent NN were adopted and comparatively analyzed, and the Elman recurrent NN was verified as the best. The error data in the system were trained and the compensation surface was hence formed to correct the image field. The mean square roots of the specimen in the x and y direction were raised from 0.2296 mm, 0.2107 mm to 0.0232 mm, 0.0265 mm respectively. The experimental results show the apparently improved scanning precision.