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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Hlavní autoři:	Biva Gyawali, Pavan Akula, Kamran Alba, Vahid Nasir
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2026-03-01
Edice:	Journal of Manufacturing and Materials Processing
Témata:	data-driven additive manufacturing synthetic data augmentation direct ink writing (DIW) explainable machine learning variational autoencoder generative adversarial network (GAN)
On-line přístup:	https://www.mdpi.com/2504-4494/10/4/118
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

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

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