RESUMO
Additive manufacturing (AM) is not necessarily a new process but an advanced method for manufacturing complex three-dimensional (3D) parts. Among the several advantages of AM are the affordable cost, capability of building objects with complex structures for small-batch production, and raw material versatility. There are several sub-categories of AM, among which is fused filament fabrication (FFF), also commonly known as fused deposition modeling (FDM). FFF has been one of the most widely used additive manufacturing techniques due to its cost-efficiency, simplicity, and widespread availability. The FFF process is mainly used to create 3D parts made of thermoplastic polymers, and complex physical phenomena such as melt flow, heat transfer, solidification, crystallization, etc. are involved in the FFF process. Different techniques have been developed and employed to analyze these phenomena, including experimental, analytical, numerical, and finite element analysis (FEA). This study specifically aims to provide a comprehensive review of the developed numerical models and simulation tools used to analyze melt flow behavior, heat transfer, crystallization and solidification kinetics, structural analysis, and the material characterization of polymeric components in the FFF process. The strengths and weaknesses of these numerical models are discussed, simplifications and assumptions are highlighted, and an outlook on future work in the numerical modeling and FE simulation of FFF is provided.
RESUMO
Additive manufacturing (AM) also commonly known as 3D printing is an advanced technique for manufacturing complex three-dimensional (3D) parts by depositing raw material layer by layer. Various sub-categories of additive manufacturing exist including directed energy deposition (DED), powder bed fusion (PBF), and fused deposition modeling (FDM). FDM has gained widespread adoption as a popular method for manufacturing 3D parts, even for heavy-duty industrial applications. However, challenges remain, particularly regarding part quality. Print parameters such as print speed, nozzle temperature, and flow rate can significantly impact the final product's quality. To address this, implementing a closed-loop quality control system is essential. This system consistently monitors part surface quality during printing and adjusts print parameters upon defect detection. In this study, we propose a simple yet effective image analysis-based closed-loop control system, utilizing serial communication and Python v3.12, a widely accessible software platform. The system's accuracy and robustness are evaluated, demonstrating its effectiveness in ensuring FDM-printed part quality. Notably, this control system offers superior speed in restoring part quality to normal upon defect detection and is easily implementable on commercially available FDM 3D printers, fostering decentralized quality manufacturing.