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.
Digital twinIntelligent manufacturingEvent-drivenQuality predictionHybrid neural network
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