Research on intelligent optimization system for injection molding process based on digital twins
基于数字孪生的注塑机成型过程智能优化系统研究

摘要

制造业智能化转型背景下，注塑机成型过程的高效优化对提升产品质量与生产效益至关重要。数字孪生技术通过构建物理实体与虚拟模型的实时映射，为解决注塑成型参数耦合复杂、质量波动大等问题提供了全新方案。依托传感器采集的压力、温度、流量等实时数据，构建高精度数字孪生模型，可实现成型过程全要素动态模拟与缺陷预判。结合智能算法对工艺参数进行迭代优化，能显著降低试错成本，提升参数调整的精准度与响应速度。该系统通过虚实交互闭环控制，有效减少成型缺陷，缩短生产周期，为注塑行业智能化升级提供技术支撑。

Abstract

In the context of the intelligent transformation of the manufacturing industry, efficient optimization of the injection molding process is crucial for improving product quality and production efficiency. Digital twin technology provides a new solution to address issues such as complex coupling of injection molding parameters and large quality fluctuations by establishing a real-time mapping between physical entities and virtual models. Relying on real-time data collected by sensors such as pressure, temperature, and flow rate, a high-precision digital twin model can be constructed to achieve dynamic simulation of all elements in the molding process and defect prediction. Combined with intelligent algorithms for iterative optimization of process parameters, it can significantly reduce the cost of trial and error and improve the accuracy and response speed of parameter adjustment. This system effectively reduces molding defects and shortens production cycles through a closed-loop control of virtual-real interaction, providing technical support for the intelligent upgrade of the injection molding industry.