Bridging Plant Biotechnology and Additive Manufacturing: A Multicriteria Decision Approach for Biopolymer Development

The increasing need for environmentally friendly substitutes for petroleum-based polymers has positioned plant-based biopolymers as potential candidates for additive manufacturing, especially in the context of fused deposition modeling (FDM). Though plant-based biopolymers have limited thermal stability, poor mechanical properties, and variable printability, limiting their industrial use. This review seeks to overcome such limitations by examining the intersection of plant biotechnology and polymer engineering, with a particular focus on the optimization of biopolymer performance through genetic engineering, recombinant DNA (rDNA) technologies, and new processing technologies. A multicriteria decision-making (MCDM) approach, integrated with machine learning (ML) algorithms, is suggested to enable optimal material selection based on printability, biodegradability, and mechanical properties. The research consolidates knowledge from recent developments in genetic modification, enzymatic polymerization, and artificial intelligence (AI)–based computational modeling to demonstrate improved polymer characteristics, such as improved tensile strength, improved interlayer adhesion, and improved thermal resistance. The main findings highlight the revolutionary role of AI-aided design loops, digital twins, and biofabrication in the achievement of scalable and high-performance biopolymers. Future research directions focus on integrating synthetic biology, autonomous laboratories, and closed-loop recycling systems toward achieving eco-efficient and next-generation additive manufacturing platforms.