An Analytically Driven Offline Digital Twin Framework for Spur Gear Forging Die Design

This study presents an upper bound energy-based offline digital twin framework developed for rapid forging load prediction during early-stage spur gear forging die design. The proposed framework integrates an analytically driven prediction model based on Upper Bound Energy Analysis (UBEA) with validated knowledge obtained from experimentally supported reference studies reported in the literature. Unlike sensor-based real-time digital twin systems, the proposed approach operates in an offline configuration and does not require continuous real-time data acquisition. The analytical framework was implemented within a computational environment to systematically estimate forging loads under different gear geometries and process conditions. The predicted forging loads were validated using representative experimental reference data reported for spur gear forging applications under comparable geometrical and processing conditions. The generated analytical dataset forms a structured knowledge base that enables virtual representation of forging load behavior within the proposed offline digital twin environment. The validation results demonstrated that the proposed framework successfully captures the overall magnitude and trend of forging loads, with an average relative deviation of approximately 6.5% across the investigated cases. Compared with conventional finite element simulations and experimental trials, the proposed framework provides a computationally efficient alternative for preliminary forging load estimation and iterative design evaluation while maintaining acceptable prediction accuracy. The developed offline digital twin framework provides a practical and scalable analytical decision-support tool for rapid gear forging die design and process planning.