Abstract
Based on the context of Industry 4.0 smart manufacturing and for the prediction of injection molding quality of automobile bumpers, this study proposes a deep learning network that combines artificial neural networks and recognizable performance evaluation methods to better achieve the prediction and control of product quality. A pressure sensor was used to monitor and collect real-time pressure data in the mold cavity of the bumper. The quality indicators reflecting the molding quality were selected, and the correlation between these indicators and the molding quality was evaluated using recognizable performance evaluation methods and Pearson’s correlation coefficient. The standard z-score was used to filter out the abnormal data in the experimental data, and the bumper critical length warpage was converted into different quality levels, and the bumper critical length warpage was defined as either “qualified” and “unqualified” in order to improve the prediction accuracy of the model. Through the experimental study of this research, the monitoring and control of bumper injection molding parameters was completed to control and improve the molding quality of the bumper.