Abstract
This paper presents a back propagation artificial neural network (BP ANN) prediction model of the gate freeze time (tgf) for injection molded polypropylenes. An orthogonal design method was applied to enhance the BP ANN performance. The test results on the performance of the BP ANN prediction model showed that it can predict tgf with reasonable accuracy. Utilizing the BP ANN prediction model, the effects of the process factors, melt temperature (Tme), fill time (tf), gate area (Ain), packing pressure (Pp), and mold temperature (Tmo) on tgf were investigated. The simulation results showed that the most important process factor affecting tgf was Tme, followed by tf, Ain, and Pp, with Tmo having the least effect. The gate freeze time increased with elevated Tme, tf, Ain, and Pp.
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