International Journal of Mechanical Engineering

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.

Digital twin; Injection molding; Intelligent optimization; Process parameters; Virtual-real mapping