Quasi-Static Hypergraph Neural Networks: A High-Performance Approach for Digital Twin Modeling of Manufacturing Process Systems with Dynamic Performance Evolution

Yiru Chen; Peiyuan Ding; Jianfu Zhang; Pingfa Feng; Xiangyu Zhang; Jianjian Wang

doi:10.1016/j.eng.2026.03.012

Engineering ›› :202603012 DOI: 10.1016/j.eng.2026.03.012

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Quasi-Static Hypergraph Neural Networks: A High-Performance Approach for Digital Twin Modeling of Manufacturing Process Systems with Dynamic Performance Evolution

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Abstract

Variations in product quality often originate from dynamic changes in the performance of manufacturing process systems. Developing digital twin models for model-based process control and optimization is a key strategy for improving quality; however, this approach is often constrained by the high cost of data acquisition in industrial environments. To address this challenge, this study proposes a quasi-static hypergraph neural network (QS-HGNN) model framework. Grounded in the assumption of microscopic quasi-static behavior and macroscopic evolution in process system performance, the proposed method abstracts process elements as nodes and uses hypergraph topology to represent complex multivariate relationships among process parameters. At the microscopic scale, static associations between nodes are quantiﬁed through discrete computation of a weight matrix, enabling the model to capture the sys- tem’s short-term performance characteristics. At the macroscopic scale, a long short-term memory net- work models the temporal evolution of these weights, thereby capturing the long-term evolution of the system. The framework integrates multimodal data fusion and physics-informed neural network con- straints to enhance generalization in small-sample scenarios. The method is empirically validated through a case study on the press-ﬁtting process of rubber bushings into track shoes for tracked vehicles. Comparative studies show that QS-HGNN achieves higher modeling accuracy than both static and dynamic hypergraph neural networks. Finally, the model is deployed within an intelligent digital twin system for real-time press-ﬁtting quality prediction and process control. Experimental results demon- strated a substantial improvement in quality performance: the qualiﬁcation rate increased from 70% to 100%, and process stability was signiﬁcantly enhanced. This research provides a high-precision, low- data-cost pathway for digital twin modeling of manufacturing process systems with progressive perfor- mance evolution, offering a scalable foundation for intelligent process optimization and quality assurance.

Keywords

Hypergraph neural networks / Digital twin / Manufacturing process systems / Dynamic performance evolution

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Yiru Chen, Peiyuan Ding, Jianfu Zhang, Pingfa Feng, Xiangyu Zhang, Jianjian Wang. Quasi-Static Hypergraph Neural Networks: A High-Performance Approach for Digital Twin Modeling of Manufacturing Process Systems with Dynamic Performance Evolution. Engineering 202603012 DOI:10.1016/j.eng.2026.03.012

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