Physics-Aligned Data Augmentation for Reliable Property Prediction in Direct Ink Writing Under Extreme Data Scarcity

Reliable property prediction in extrusion-based additive manufacturing remains challenging under extreme data scarcity (e.g., sample size of <50), particularly when experiments are constrained by designed studies such as Taguchi orthogonal arrays. In direct ink writing of lignocellulosic composites, limited experimental runs restrict the development of predictive models capable of guiding formulation and process optimization. This study introduces a physics-consistent data augmentation framework to enhance predictive reliability while preserving material-consistent behavior. Synthetic data are evaluated using four criteria: sensitivity to augmentation size, distributional consistency with experimental observations, stability with respect to boosting depth in regression modeling, and preservation of physics-consistent factor hierarchies through interpretability analysis. The framework is validated using compressive strength data from direct ink writing experiments conducted under an extremely small data regime. Results show that augmentation performance depends on the augmentation scale and model capacity. Variational autoencoder-based augmentation produced more stable and physically consistent predictions than conditional tabular generative adversarial networks in this application. Increasing predictive accuracy alone, or applying excessive augmentation, can distort material hierarchies and reduce physics consistency. The proposed evaluation framework supports reliable and interpretable property prediction in additive manufacturing when experimental data are severely limited.