Citation

Ng, Jack Kok Wah (2025) AI-Driven 3D and 4D Food Printing: Innovations for Sustainability, Personalization, and Global Applications. Food Reviews International. pp. 1-29. ISSN 8755-9129

Text AI-Driven 3D and 4D Food Printing_ Innovations for Sustainability, Personalization, and Global Appli.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

The review highlights the transformative role of Artificial Intelligence (AI) and machine learning in advancing 3D food printing (3DFP), focusing on improved customization, print quality, consistency, and operational efficiency. While progress has been made, full AI integration across the 3DFP workflow particularly in real-time monitoring and adaptive manufacturing remains limited. The study emphasizes AI’s potential to reduce food waste, enable personalized nutrition, and enhance 3D/4D printing through smart materials and optimized ink formulations. Using the PRISMA framework, recent studies were analyzed to show how AI-driven techniques support print parameter optimization, material behavior prediction, and real-time feedback. Reinforcement learning, for example, can reduce material waste by up to 25%, especially with high-cost or sustainable ingredients. Innovations such as nanomaterials and 4D food printing are expanding applications into areas like personalized healthcare. Despite these advancements, challenges persist in printability, data processing, material compatibility, and AI reliability. The review underscores the need for standardized datasets, biocompatible materials, and regulatory clarity. Future directions include developing intelligent closed-loop systems and fostering interdisciplinary collaboration to improve scalability and robustness. Overall, AIenhanced 3DFP shows strong potential to revolutionize food systems by delivering customized, sustainable, and nutritionally precise solutions.

Item Type:	Article
Uncontrolled Keywords:	Machine learning, optimization
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Management (FOM)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	27 May 2025 08:42
Last Modified:	27 May 2025 08:42
URII:	http://shdl.mmu.edu.my/id/eprint/13830

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