Variable inlet guide vane (VIGV) control is among the most energy-efficient flow regulation methods for axial pumps because it adjusts the inlet swirl angle directly while preserving high hydraulic efficiency. However, unlike rotational-speed control, whose performance curves scale straightforwardly through the affinity laws, VIGV control alters the intrinsic shape of the head-flow (H-Q) curve at each vane angle and therefore requires angle-specific prediction. This study proposes a transition-informed Gaussian process regression (TI-GPR) model that augments standard GPR by explicitly incorporating the gradient sign-reversal point of the S-shaped characteristic curve through adaptive region splitting and sigmoid-based blending. A four-factor evaluation covering CV strategy, training-data sparsity, input dimensionality, and model type shows that, even when only Q–H data are available (2D input), TI-GPR lowers the relative MAPE by 17.525% under sparse interpolation and by 35.938% under extrapolation relative to the baseline GPR model. Adding valve-position information (3D input) improves the accuracy of both models further, and TI-GPR retains its advantage. These results demonstrate that a minimal structural modification embedding the stability-gradient transition boundary can yield substantial predictive gains, particularly in data-scarce regimes.

Digital twin technologies in manufacturing have evolved into dynamic, data-synchronized systems that facilitate real-time monitoring and control. Given that machining involves closely interconnected multi-physics behaviors, the effectiveness of a digital twin largely relies on the accuracy and reliability of its underlying process models. This review systematically evaluates three primary paradigms for machining process modeling in digital twins: physics-based, data-driven, and hybrid approaches. Physics-based models provide interpretability and physical consistency but are hindered by high computational costs and limited adaptability to changing conditions. In contrast, data-driven models offer real-time capabilities and adaptive learning but face challenges related to data scarcity and black-box behavior. Hybrid modeling has emerged as the most promising approach, combining physical laws with machine learning through techniques such as parameter correction, physics-guided learning, and state-estimation-based intelligent control. Recent research demonstrates significant advancements in predictive performance, adaptability, and computational efficiency across various machining applications, underscoring the effectiveness of new process modeling strategies for digital twins. However, challenges remain, including multi-physics integration, model reduction for real-time deployment, and autonomous self-updating in data-limited scenarios. The review concludes that hybrid models present the most viable pathway to achieving high-fidelity, self-adaptive, and trustworthy digital twins for autonomous manufacturing.