Giheung Choi ¹ and G.S. Choi

Department of Mechanical Systems Engineering, Hansung University, 389 Samsun-dong 2-ga Sungbuk-gu, Seoul, Korea (136-792) TEL: +82-2-760-4322 FAX : +82-2-760-4217

School of Electrical and Electronic Engineering, Seoul City University, 90 Jeonnong-dong, Dongdaemoon-gu, Seoul, Korea (130-743) TEL: +82-2-210-2526 FAX: +82-2-213-8317

 

Abstract

 

Implementing CIM (Computer Integrated Manufacturing) often requires models of manufacturing processes to be devised for determining optimal process parameters and designing adaptive control systems. Despite the progress made in analytical (mechanistic) modeling, however, empirical models derived from experimental data are more frequently used in practice. This paper describes the development of a neural network model for injection molding process. The model uses CAE (Computer Aided Engineering) analysis data based on Taguchi method which ensures the effectiveness of the model and the efficient learning by the network. In view of the robust process design, only those input parameters that are not overly sensitive to external disturbances but sensitive enough to injection performance are identified using analysis of variance. The model is compared with the traditional polynomial regression model.