Decentralized Supply Chains Using Fused Disposition Modeling as a Framework: Optimization Using a Machine Learning Approach

The globally used additive manufacturing technique called Fused Deposition Modeling plays a central role in advancing dematerialized logistics by enabling on-demand production and minimizing material waste. The integration of Artificial Intelligence (AI) into FDM processes has introduced promising avenues to improve efficiency, accuracy, and sustainability. Expressly, researchers have proved in what ways machine learning algorithms can upgrade printing parameters, initiating enhanced product quality and lower defects. In the context of dematerialized logistics, the PRISMA methodology mentioned in this review is set to maintain a structured analysis of the junction between AI and FDM. Exhaustive research of analyzed studies issued from 2009 to 2024 through databases like Scopus, Web of Science, and IEEE Xplore demonstrate an expanding reliance on AI techniques like neural networks and genetic algorithms. All these mentioned methods are used to approach challenges such as print quality inconsistencies, material overuse, and structural weaknesses. The outcome shows the prospect of AI to reshape FDM, but major obstacles remain present: many problems, such as the scalability of models and their integration into existing logistical frameworks, need further studies and research. As demonstrated, this review gives an inclusive perspective on the actual progress and highlights the main directions for what lies ahead to improve FDM processes in logistics.