MLP neural network models of CMM measuring processes

Abstract

The objective of this study is to show how a multi-layer perceptron (MLP) neural network can be used to model a CMM measuring process. To date, most MLP-based process models have been established for process mean only. An innovative approach is proposed to model simultaneously the mean and the variation of a CMM process using one integrated MLP architecture. Therefore, the MLP-based model obtained captures not only the process mean but also the process variation information. Selected issues related to neural network training are also discussed. Specifically, the guideline that was proposed by Mirchandani and Cao (1989) for selecting a number of hidden neurons is tested to determine the effects of the number of hidden neurons. The performances of two different learning algorithms -back-propagation with momentum factor (BPM) and the Fletcher-Reeves (FR) algorithm -are studied in terms of CPU time, training error, and generalization error.