A inference mechanism for polymer processing using rough-neuro fuzzy network

Abstract

There is an increasing tendency in the worldwide automotive market to consume polymeric materials, because of their processability and low cost in high volumes. This need motivates the search for technological improvements to the material performance, even at the product development stage. The purpose of this paper is to predict the cycle time of an injected part according to its molding parameters using a Rough-Neuro Fuzzy Network. The methodology involves the application of Fuzzy Sets to define an inference mechanism that inserts human knowledge about polymer processing into a structured rule basis. The attributes of the molding parameters are described using membership functions and reduced by Fuzzy rules. The Rough Sets Theory identifies the attributes that are important and the Fuzzy relations influence the Artificial Neural Network (ANN) surface response. Thus, the rule basis filtered by Rough Sets is used to train a back-programmed Radial Basis Function (RBF) and/or a Multilayer Perceptron (MLP) Neuro Fuzzy Network. In order to measure the performance of the proposed Rough-Neuro Fuzzy Network, the responses of the unreduced rule basis are compared with the reduced rule basis. The results show that by making use of the Rough-Neuro Fuzzy Network, it is possible to reduce the need for expertise in the construction of the Fuzzy inference mechanism. © 2010 Springer-Verlag Berlin Heidelberg.