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

Journal of Manufacturing and Materials Processing 2026 Open access

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Authors: Biva Gyawali, Pavan Akula, K. Alba, Vahid Nasir

DOI: 10.3390/jmmp10040118

Cited by: 2

References: 23

Abstract

Reliable property prediction in extrusion-based additive manufacturing remains challenging under extreme data scarcity (e.g., sample size of

Additive Manufacturing and 3D Printing Technologies Generative Adversarial Networks and Image Synthesis Machine Learning in Materials Science

References 23