Cross-machine predictions of the quality of injection-molded parts by combining machine learning, quality indices, and a transfer model

The achievement of consistent molding quality, which is critical in injection molding, is heavily reliant on the reasonable control of processing materials, molds, machines, process parameters, and environmental conditions. Notably, new molds usually require a trial molding process before being delivered to relevant machines for online production. However, performance differences between machines make it challenging to maintain consistent molding quality, and suitable adjustments must be made to machine parameters to compensate for these differences. Therefore, cross-machine product quality prediction is critical for accurately forecasting product quality across different machines in a manufacturing process and thus for ensuring consistent quality, few defects, and optimized production. To avoid the considerable time and high cost required for quality inspection and to improve production efficiency, this study developed a multilayer perceptron (MLP) model combined with quality indices to predict molding quality. This paper describes how the developed model predicts product quality for the same mold in different machines. The procedure of the proposed MLP model involves four steps. First, data are prepared, features are extracted (extraction of quality indices), and the model is trained on an actual injection molding machine (machine A). Second, the developed MLP model establishes the relationships between the process parameters, quality indices, and product quality for machine A. Third, Moldex3D Studio, which is a software program for simulating injection molding, is employed to generate production data for a virtual injection molding machine (machine B). Finally, a transfer model is used to fit the quality indices of machines A and B so that the MLP model can directly predict the product quality (in terms of weight and geometric dimensions) for machine B on the basis of the quality indices generated using the process parameters of machine B. Experimental results indicate that the developed MLP model can accurately predict the weight and dimensions of products manufactured using different injection molding machines. In particular, the average error in predicting the product quality for machine B was found to be smaller than 0.5%, which indicates the feasibility of the developed model for cross-machine product quality prediction.