Modeling the Effect of Injection Molding Process Parameters on Warpage Using Neural Network Theory

Abstract

A back propagation artificial neural network (BPANN) prediction model for warpage of injection-molded polypropylene was developed based on an orthogonal design method. The BPANN model was trained by the input and output data obtained from the moldflow software platform simulations. It is proved that the BPANN model can predict the warpage with reasonable accuracy. Utilizing the BPANN model, the effects of the process parameters, packing pressure (Pp), melt temperature (T me ), mold temperature (T mo ), packing time (t p ), cooling time (t c ), and fill pressure (p f ), on the warpage were investigated. The most important process parameter affecting the warpage was Pp, and the second most important was T me . The rest of the process parameters, T mo , t p , t c , and p f , were found to be relatively less influential. Warpage increased with elevating T mo . In contrast, an increase in Pp and T me caused the warpage to decrease.

Overview of the Injection Moulding Optimisation for Shrinkage Defect

“…Not just that, the product produce are mostly variety in complex shape [6], thin, small and light. Focusing on the product quality, this process is gaining more and more attention because of the promising standard measure which focusing on the material used [7], part designing and parameters setting [8][9][10] in influencing the process so that it can be controlled. Despite that, there are some shortcoming occur alongside the process especially in maintaining the quality of the parts and cost of the products.…”