首页|事件驱动式产品数字孪生系统构建和质量预测

事件驱动式产品数字孪生系统构建和质量预测

扫码查看
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.
Event-Driven Product Digital Twin System Construction and Quality Prediction
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

向峰、廖可

展开 >

武汉科技大学冶金装备及其控制省部共建教育部重点实验室,武汉 430081

武汉科技大学机械传动与制造工程湖北省重点实验室,武汉 430081

数字孪生 智能制造 事件驱动 质量预测 混合神经网络

国家自然科学基金

51975431

2024

航空制造技术
北京航空制造工程研究所

航空制造技术

CSTPCD北大核心
影响因子:0.403
ISSN:1671-833X
年,卷(期):2024.67(11)