NEURAL NETWORK METHOD TO RECONSTRUCT THE FREEFORM SURFACE

This article presents an artificial neural network approach to solve the problem of reconstruction and manufacture of freeform surfaces in reverse engineering. Taking advantage of the global minimum property of Simulated Annealing Procedure, a technique is proposed to accept a temporally failed training result in accordance to probability. Using this technique, the training can jump out of the local minimum and converge to the global minimum. The method is better than the algorithm given in the article, when it is used to solve the problem of reconstruction and manufacture of freeform surfaces.