基于数字孪生的压铸过程监控和产品质量预测

Die Casting Process Monitoring and Product Quality Prediction Based on Digital Twin

为解决汽车零部件压铸成型过程的质量预测问题,提出一项基于数字孪生技术的质量预测方法,旨在实现对压铸过程的实时监控、信息管理、以及生产数据的充分利用。首先建立了虚拟—真实数字孪生模型;随后引入多源数据采集和管理方法,结合数据驱动技术实现压铸单元的虚实同步映射;最终,针对压铸成型的料饼厚度这一关键工艺参数,提出基于Stacking集成学习的压铸产品质量预测算法。通过在某汽车零部件制造企业的压铸车间进行实验,验证了技术方法的可行性,为解决复杂压铸制造过程的监控和质量预测问题提供了新思路。

In order to solve the problem of quality prediction in the die-casting process of auto parts,this study proposes a quality prediction method based on digital twin technology,which aims to realize the real-time monitoring,information management,and full use of production data in the die-casting process.Firstly,a virtual-real digital twin model is established,and then a multi-source data acquisition and management method is introduced,and the virtual and real synchronous mapping of the die-casting unit is realized by combining data-driven technology.Finally,aiming at the key process parameter of cake thickness of die-casting molding,a die-casting product quality prediction algorithm based on Stacking ensemble learning was proposed.Through experiments in the die-casting workshop of an auto parts manufacturing enterprise,the feasibility of the technical method is verified,and a new idea is provided for solving the monitoring and quality prediction problems of complex die-casting manufacturing process.