Abstract Transfer learning (TL) has emerged as an effective approach in intelligent manufacturing by reusing knowledge from related domains to address challenges such as data scarcity, product diversity, and frequent process reconfiguration. However, the effectiveness of TL critically depends on selecting source domains with high transferability. Existing transferability metrics primarily emphasize algorithmic or data-driven aspects and often lack interpretability for manufacturing practice. This study proposes a framework that integrates context-aware transferability score (CTS), including structural alignment, monotonicity consistency, and key parameter analysis, with a data-based transferability score (DTS) that captures distributional similarity through Maximum Mean Discrepancy (MMD), Wasserstein Distance (WD), and Jensen–Shannon (JS) divergence. These components are combined into a holistic transferability score (HTS), which balances process-informed interpretability with data-driven rigor. Validation on a real-world battery anode manufacturing case and complementary synthetic stress tests demonstrates that the proposed framework provides explainable, computationally efficient, and reliable guidance for source domain selection. The proposed method establishes a systematic and interpretable foundation for transfer learning in industrial environments, mitigating negative transfer and supporting informed decision-making in intelligent manufacturing.
Wang et al. (Tue,) studied this question.