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Application of artificial neural networks for the optimal design of sheet molding compound (SMC) compression molding
Author(s) -
Twu JunTien,
Lee L. James
Publication year - 1995
Publication title -
polymer composites
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.577
H-Index - 82
eISSN - 1548-0569
pISSN - 0272-8397
DOI - 10.1002/pc.750160508
Subject(s) - artificial neural network , molding (decorative) , compression molding , process (computing) , optimal design , computer science , design of experiments , factorial experiment , materials science , artificial intelligence , machine learning , composite material , mathematics , mold , operating system , statistics
A sequential design optimization scheme based on artificial neural networks (ANN) is proposed. It is a combination of an ANN model and a nonlinear programming algorithm. The proposed scheme is implemented with network training, optimization, and sheet molding compound (SMC) process simulation in a closed loop. A “cyclic coordinate search” technique is employed to initiate the optimization process, to collect training data for the neural network model, and to perform a preliminary design sensitivity analysis. Emphasis is placed on the development of an integrated, automatic optimization‐simulation design tool that does not rely on the designer's experience and interpretation. Testing results based on the design of heating channels in an SMC compression molding tool show that the optimal design can be achieved with fewer data points than other methods, such as factorial design. The efficiency of the ANN method would be greater as the number of design variables grows.