by Jack Ng Kok Wah
ABSTRACT
This systematic review explores the integration of artificial intelligence (AI) and machine learning (ML) in advancing 3D food printing (3DFP), with emphasis on customization, optimization, and emerging innovations. Recent studies (2023–2024) were reviewedto evaluate AI-enhanced 3DFP, focusing on optimization techniques, AI-driven monitoring systems, and 4D printing developments.AI-driven 3D food printing enhances food design precision, material versatility, and customization while promoting sustainability through reduced waste and novel bio-ingredients. Advances in multimaterial printing, shape-shifting capabilities, and nanotechnology integration further improve texture, nutrition, and consumer appeal, setting the stage for transformative impacts across gastronomy, healthcare, and food technology sectors. ML methods such as reinforcement learning and deep learning significantly enhance parameter optimization, material behavior, and print quality. AI facilitates real-time adjustments to extrusion force, viscosity, and structural integrity. Applications span personalized nutrition, tissue engineering, and gastronomy. 4D printing introduces dynamic, shape-shifting capabilities. Technological limitations, data quality issues, and regulatory barriers remain. Key concerns include ink printability, structural stability, and consistency.AI and 3DFP present a transformative synergy in food technology, promoting sustainability, personalization, and precision. Future research should address scalability through biocompatible materials and standardized datasets.
