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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,...

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Tallennettuna:
Päätekijät: Biva Gyawali, Pavan Akula, Kamran Alba, Vahid Nasir
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: MDPI AG 2026-03-01
Sarja:Journal of Manufacturing and Materials Processing
Aiheet:
data-driven additive manufacturing
synthetic data augmentation
direct ink writing (DIW)
explainable machine learning
variational autoencoder
generative adversarial network (GAN)
Linkit:https://www.mdpi.com/2504-4494/10/4/118
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