In view of the problems of lagging and poor predictability for ship assembly and welding quality control, the digital twin technology is applied to realize the quality prediction and control of ship group product. Based on the analysis of internal and external quality factors, a digital twin-based quality prediction and control process was proposed. Furthermore, the digital twin model of quality prediction and control was established, including physical assembly and welding entity, virtual assembly and welding model, the quality prediction and control system, and twin data. Next, the real-time data collection based on the Internet of Things and the twin data organization based on XML were used to create a virtual-real mapping mechanism. Then, the machine learning technology is applied to predict the process quality of ship group products. Finally, a small group is taken as an example to verify the proposed method. The results show that the established prediction model can accurately evaluate the welding angular deformation of group products and also provide a new idea for the quality control of shipbuilding.

中文翻译：

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基于数字孪生的船组产品装配焊接过程质量预测与控制

针对船舶装配和焊接质量控制滞后、可预测性差的问题，应用数字孪生技术实现船组产品质量预测和控制。在分析内外质量因素的基础上，提出了一种基于数字孪生的质量预测与控制流程。进一步建立了质量预测与控制的数字孪生模型，包括物理装配与焊接实体、虚拟装配与焊接模型、质量预测与控制系统、孪生数据。接下来，利用基于物联网的实时数据采集和基于XML的孪生数据组织，创建虚实映射机制。然后，应用机器学习技术对船组产品的过程质量进行预测。最后，以一个小组为例来验证所提出的方法。结果表明，所建立的预测模型能够准确评估成组产品的焊接角变形，也为造船质量控制提供了新的思路。