Development of a hybrid neural network system for prediction of process parameters in injection moulding

In this paper, the attempts made by the authors to develop an artificial neural network system for prediction of injection moulding process parameters is presented. In this work, attempts have been made to determine the process parameters that could affect injection moulding process based on governing equations of the 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 were collected out of which 94 were used to train the network using MATLAB and the remaining 20 for testing the network. Two algorithms are used during the training phase, namely the error-back-propagation algorithm and the Levenberg–Marquardt approximation algorithm. Results showed that the latter algorithm is more suitable for this application since the Leverberg’s algorithm converged rapidly with lesser training cycles when compared to 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 and found that the overall error is 0.93% with a deviation of 3.93%.