Research on Product Cost Estimation Method of Machine Tool Based on Principal Component Analysis and Artificial Neural Network

摘要

This paper proposed a product cost estimation method based on principal component analysis (PCA) and artificial neural network (ANN) for generalized modular design of machine tool. In the first stage, PCA was applied to identify the principal components of product modular features, which was conducted by analyzing the product cost components and their influencing factors driven by features of modules firstly, and then by calculating the eigenvalue and eigenvector of correlation coefficient matrix to reduce the dimension of the data, later by defining the first few principal components which contain most of the feature variables. In the second stage, the mapping from the restructured product modular feature to the product cost was established by general regression neural network (GRNN). At last, the simulation results demonstrate that the proposed algorithm is effective and speedy.