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Shrinkage and warpage prediction of injection‐molded thin‐wall parts using artificial neural networks
Author(s) -
Liao S. J.,
Hsieh W. H.,
Wang James T.,
Su Y. C.
Publication year - 2004
Publication title -
polymer engineering and science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.503
H-Index - 111
eISSN - 1548-2634
pISSN - 0032-3888
DOI - 10.1002/pen.20206
Subject(s) - shrinkage , taguchi methods , artificial neural network , materials science , mold , composite material , process (computing) , backpropagation , structural engineering , engineering , artificial intelligence , computer science , operating system
This study demonstrates the successful use of back‐propagation artificial neural networks (BPANNs) in predicting the shrinkage and warpage of injection‐molded thin‐wall parts. The effects of structural parameters of a BPANN on the predictionaccuracy and the capability of a BPANN in determining the optimal process condition are also discussed. The training and testing data are obtained experimentally based on a Taguchi L 27 (3 13 ) test schedule. The results show that the trained BPANN can successfully predict the shrinkage and warpage of injection‐molded thin‐wall parts. Comparing the prediction accuracies of the trained BPANN and C‐Mold software, it is noted that the trained BPANN predicts more accurately. In terms of determining the optimal process condition for minimizing the shrinkage and warpage of injected thin‐wall parts, the trained BPANN is also shown to give a better optimal process condition than Taguchi's method. Polym. Eng. Sci. 44:2029–2040, 2004. © 2004 Society of Plastics Engineers.