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A New Surface Reconstruction Method in Reverse Engineering

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4 Author(s)
Wu Xue-mei ; Sch. of Mech. & Electr. Eng., Harbin Inst. of Technol., Harbin, China ; Li Gui-xian ; Shan De-bin ; Zhao Wei-min

Points cloud surface reconstruction method in reverse engineering was proposed, radial basis function neural network (RBFNN) was designed, and simulated annealing arithmetic was adopted to adjust the network weights. MATLAB program was compiled, experiments on points cloud data have been done employing the program. All experiments have shown that the arithmetic can efficiently approach the surface with 10-4 mm error precision, also the learning speed is quick and reconstruction surface is smooth. Trainings have been done with other networks in comparison. The sum squared error is 4.6802×10-8 mm using the algorithmic proposed in this paper, the one is 1.1014×10-6 mm under the same parameters employing RBFNN only. Back-propagation learning algorithm network does not converge until 3500 iterative procedure, and exactness design RBFNN is time-consuming. The arithmetic can approach nonlinear function by arbitrary precision, and also keep the network from getting into partial minimum.

Published in:

Natural Computation, 2009. ICNC '09. Fifth International Conference on  (Volume:2 )

Date of Conference:

14-16 Aug. 2009