Digital Twin Incorporating Deep Learning and MBSE for Adaptive Manufacturing of Aerospace Parts

Zhibo Yang, Xiaodong Tong, Haoji Wang, Zhanghuan Song, Rao Fu, Jinsong Bao

With the growing demand for diverse and high-volume manufacturing of composite material parts in aerospace applications, traditional machining methods have faced significant challenges due to their low efficiency and inconsistent quality. To address these challenges, digital twin (DT) technology offers a promising solution for developing automated production systems by enabling optimal configuration of manufacturing parameters. However, despite its potential, the widespread adoption of DT in complex manufacturing systems remains hindered by inherent limitations in adaptability and inter-system collaboration. This paper proposes an integrated framework that combines Model-Based Systems Engineering (MBSE) with deep learning (DL) to develop a digital twin system capable of adaptive machining. The proposed system employs three core components: machine vision-based process quality inspection, cognition-driven reasoning mechanisms, and adaptive optimization modules. By emulating human-like cognitive error correction and learning capabilities, this system enables real-time adaptive optimization of aerospace manufacturing processes. Experimental validation demonstrates that the cognition-driven DT framework achieves a defect recognition accuracy of 99.59% in aircraft cable fairing machining tasks. The system autonomously adapts to dynamic manufacturing conditions with minimal human intervention, significantly outperforming conventional processes in both efficiency and quality consistency. This work underscores the potential of integrating MBSE with DL to enhance the adaptability and robustness of digital twin systems in complex manufacturing environments.