事件驱动式产品数字孪生系统构建和质量预测

Event-Driven Product Digital Twin System Construction and Quality Prediction

复杂制造过程中面临着场景复杂性和多元动态事件带来的挑战。为提高复杂产品质量预测的准确性,将数字孪生与事件驱动相结合,提出了一种事件驱动式产品数字孪生系统框架,建立了事件驱动式产品制造多维孪生模型,利用数字孪生模型来模拟实际制造过程中的各种场景,并结合关键事件信息,实现了对产品质量的更精准预测。然后,针对事件序列中的时间依赖关系,结合卷积神经网络(CNN)、双向门控循环单元(BiGRU)和自注意力机制(Self-attention),构建了基于混合神经网络的产品质量预测模型。最后,以双离合变速箱(Dual clutch transmission,DCT)装配为例,阐述了事件驱动式变速箱装配质量预测数字孪生运行模式;并通过对比传统单模型的预测方法,验证了所提出的质量预测模型的准确性。

Complex manufacturing processes face challenges posed by scenario complexity and multiple dynamic events.In order to improve the accuracy of complex product quality prediction,an event-driven product digital twin system framework is proposed by combining digital twin and event-driven,and an event-driven product manufacturing multidimensional twin model is established,which is utilized to simulate various scenarios in the actual manufacturing process and combined with the key event information to achieve a more accurate prediction of product quality.Then,the product quality prediction model based on hybrid neural network is constructed by combining convolutional neural network(CNN),bidirectional gated recurrent unit(BiGRU)and self-attention mechanism for the time-dependent relationship in the event sequence.Finally,the event-driven digital twin operation model for quality prediction of transmission assembly is illustrated by taking dual clutch transmission(DCT)assembly as an example;and the accuracy of the proposed quality prediction model is verified by comparing with the traditional single-model prediction method.