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 system’s short-term performance characteristics. At the macroscopic scale, a long short-term memory network 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 constraints 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 demonstrated 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 performance evolution, offering a scalable foundation for intelligent process optimization and quality assurance.
Chen et al. (Wed,) studied this question.