This paper aims to propose a digital twin-driven (DTD) approach that consists of the machining data (MD) in twin data (TD), design of MD acquisition methodology, construction of intelligent algorithm, real-virtual data interaction analysis and fusion technology, which improvs the predictability and management of on-line quality control of marine diesel engine (MDE) critical parts. Firstly, this paper introduces the theoretical framework of DTD on-line quality control in machining process. Secondly, we construct the process of DTD on-line quality control and introduce the digital twin model of on-line quality control based on TD-driven; the operation of data-driven quality on-line control based on digital twin including description and modeling of MD; acquisition of MD based on digital twin; TD-driven on-line tool life prediction and data fusion on-line machining parameters optimization methods. Finally, a case study is applied to validate the accuracy and availability of the DTD approach. The proposed approach provides a new way for the on-line quality control of MDE critical parts in machining process.

本文旨在提出一种数字双驱动（DTD）方法，该方法包括双数据（TD）中的加工数据（MD），MD采集方法的设计，智能算法的构建，实虚拟数据交互分析和融合技术。 ，提高了船用柴油机（MDE）关键零件的在线质量控制的可预测性和管理能力。首先，介绍了机械加工过程中DTD在线质量控制的理论框架。其次，构建了DTD在线质量控制的过程，并引入了基于TD驱动的在线质量控制数字孪生模型。基于数字孪生的数据驱动质量在线控制的操作，包括机床数据的描述和建模；收购基于数字孪生的MD；TD驱动的在线刀具寿命预测和数据融合在线加工参数优化方法。最后，通过案例研究来验证DTD方法的准确性和可用性。所提出的方法为加工过程中MDE关键零件的在线质量控制提供了一种新方法。