Development and Application of an AI-Assisted System for Enhancing the Efficiency of Plastic Mold Design

With advances in injection molding and diverse manufacturing demands, accelerating mold development and improving quality have become key challenges. This study proposes an AI-assisted mold design system integrating database matching and predictive models to support engineers in early-stage analysis and parameter prediction, thereby enhancing efficiency and yield. The system includes three modules: design analysis, gate recommendation, and result prediction. In design analysis, geometric and structural features of new models are compared with historical cases to identify relevant references, while material and parameter data improve prediction accuracy. For gate design, case-based matching suggests configurations for efficient selection of gate types and locations. For result prediction, AI models trained on uploaded project data enable real-time parameter adjustment and visualization, reducing trial iterations and costs. By combining database knowledge with AI, the system provides early-stage decision support, showing strong potential to improve both design efficiency and molding quality.