title: Neural network approach for modification and fitting of digitized data in reverse engineering.
abstract: Reverse engineering in the manufacturing field is a process in which the digitized data are obtained from an existing object model or a part of it, and then the CAD model is reconstructed. This paper presents an RBF neural network approach to modify and fit the digitized data. The centers for the RBF are selected by using the orthogonal least squares learning algorithm. A mathematically known surface is used for generating a number of samples for training the networks. The trained networks then generated a number of new points which were compared with the calculating points from the equations. Moreover, a series of practice digitizing curves are used to test the approach. The results showed that this approach is effective in modifying and fitting digitized data and generating data points to reconstruct the surface model.
authors.affiliation: Institute of Advanced Manufacturing Engineering, Zhejiang University, Hangzhou 310027, China. huaju@zju.edu.cn
authors.forename: Hua
authors.initials: H
authors.lastname: Ju
authors.forename: Wen
authors.initials: W
authors.lastname: Wang
authors.forename: Jin
authors.initials: J
authors.lastname: Xie
authors.forename: Zi-chen
authors.initials: ZC
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D017076:Computer-Aided Design; D004738:Engineering / Q000379:methods; D004867:Equipment Design / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted