Prediction of processing parameters for injection moulding by using a hybrid neural network

In the present work attempts have been made to determine the process parameters that could affect an injection moulding process based on governing equations of the mould-filling process. Focus is then directed to parameters that require the use of trial and error methods or other complex software to determine the process parameters. The two parameters that are predicted from the developed network are injection time and injection pressure. In this work, the training data are generated by simulation using C-MOLD flow simulation software. A total of 114 data points under different process conditions were collected out of which 94 data points were used to train the network using MATLAB and the remaining information was used for testing the network. Two algorithms are used during the training phase, namely the error back-propagation algorithm and the LevenbergMarquardt approximation algorithm. Results showed that the latter algorithm is more suitable for this application since the Levenberg algorithm converged rapidly with fewer training cycles when compared with the error back-propagation algorithm. The accuracy of the developed network has been tested by predicting the injection pressure and injection time for few engineering components.