Zhu Xiang

Mrs. Zhu Xiang


Research Focus & Metrics

Academic Career Length

1Year (2026-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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24 August 2026 in Systems
Verified
Journal Article
Open access
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ZXZhu Xiang
Zhu Xiang
MLMinghao Li
Minghao Li
TLTianyang Lei
Tianyang Lei
KYKewei Yang
Kewei Yang
GSGuopeng Song
Guopeng Song
JJJiang Jiang
Jiang Jiang
Cross-phase collaborative management in complex product systems (CoPS) development is inherently challenged by heterogeneous organizational coupling and stochastic disturbances. Although digital twin (DT) and model-based systems engineering (MBSE) technologies provide foundations for physical–virtual synchronization and model traceability, existing approaches remain fragmented in three respects: insufficient requirements-traceable architectural integration, limited cross-phase semantic interoperability and runtime evolution, and weak operational links between semantic reasoning and adaptive decision models. To address these gaps, this paper proposes an MBSE-driven digital twin framework with semantic enhancement. First, a four-layer architecture is derived using the MagicGrid methodology, encompassing physical–virtual mapping, semantic reasoning, decision support, and service interaction. Second, a collaboration-oriented SysML profile is developed to standardize the representation of tasks, resources, materials, disturbances, and management constraints across engineering phases. Third, a knowledge-driven adaptive collaboration mechanism maps runtime disturbance inputs into semantic states, propagates their cross-phase impacts, supports process-topology reconfiguration, and generates decision-ready constraints for adaptive management. A case-based prototype for aero-engine turbofan blade development demonstrates the feasibility of the mapping–reasoning–decision chain and provides case-level evidence of improved cross-phase coordination under controlled disturbance scenarios. The results indicate a feasible engineering pathway from perceptive DT functions toward reasoning-enabled collaborative decision support.