Hybrid Multimodal Surrogate Modeling and Uncertainty-Aware Co-Design for L-PBF Ti-6Al-4V with Nanomaterials-Informed Morphology Proxies

Reliable property prediction and process selection in laser powder bed fusion are hindered by small, set-level datasets in which key morphology descriptors are intermittently missing, limiting both generalization and actionable co-design. A hybrid multimodal surrogate strategy is introduced that couples engineered process physics features with morphology proxies through a deployable two-stage embedding module and gradient-boosted tree regressors. Set-resolved inputs are assembled from L-PBF parameters, linear energy density and related energy-density variants, pore and prior-β grain summary statistics, and stress–strain-derived descriptors, followed by missingness-aware feature filtering, median imputation, and 5-fold GroupKFold evaluation grouped by setᵢd, with morphology embeddings learned on training folds and predicted when absent. Across six targets, the final deployable models achieve an RMSE/R2 of 11. 07 MPa/0. 895 (yield), 13. 88 MPa/0. 873 (UTS), 0. 677%/0. 861 (elongation), and 2. 38 GPa/0. 663 (modulus), while roughness and hardness remain challenging (RMSE 2. 31 μm and 16. 54 HV; R2 about 0. 12 and 0. 11). These surrogates enable constraint-aware candidate generation that identifies a concise set of manufacturing recipes balancing strength and surface objectives under uncertainty-aware screening. The resulting framework provides a practical blueprint for multimodal, small-data additive manufacturing studies and can be extended to richer microstructure measurements and prospective validation to accelerate functional and biomedical alloy development.