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The Construction and Analysis of a Prediction Model for Combining the Taguchi Method and General Regression Neural Network for Injection Moulding

Plastics always show mould deformation after the injection moulding process and this can cause dimensional errors. The extent of a dimensional error can be controlled effectively by choosing adequate processing parameters. The target of this work was to reduce the dimensional error in the hexagonal screw diameter when processing polyether ether ketone (PEEK), and to improve the polymer's quality. First, we selected the factor levels according to the chosen quality characteristics. We decided that a prediction model for the injection process could be constructed using the Taguchi quality method and a general regression neural network (GRNN). The optimum conditions determined by the Taguchi method can be modified by the neural network. The injection moulding product can achieve a reduced dimensional error by adjusting the factor levels using this combined approach.