Advancements in fused deposition modelling (FDM): a comparison of advanced composite materials, optimisation strategies, and AI/ML integration

Abstract

Fused deposition modelling (FDM) is a modern manufacturing technique that simplifies the manufacturing process by removing complexities with traditional methods, allowing the designer to design customised parts with greater flexibility. In this review, different composite filaments used in FDM are compared based on their mechanical strengths to understand the effect of varying material reinforcements. Additionally, biodegradable filaments are also explored as a renewable and sustainable option. This paper also compares different methods of optimising the FDM process, including Response Surface Methodology (RSM), Taguchi, and Artificial Intelligence (AI) techniques, to improve the quality and strength of 3D-printed parts. Among the reviewed biodegradable filament materials, it was concluded that Polyethylene Terephthalate Glycol (PETG) composites demonstrated a more versatile performance, offering superior mechanical strength and thermal resistance. In addition to that, using bio-filler materials in PETG enhances sustainability without sacrificing functionality. Moreover, the analysis revealed that integrating AI techniques into FDM, such as for property prediction, defect detection, and topology optimisation, can enhance the accuracy of outcomes, provided that appropriate AI model is selected for each task. This review establishes a base for researchers and practitioners considering the adoption of AI in FDM while outlining potential directions for future work in this field.