Monitoring of fused filament fabrication (<scp>FFF</scp>): An infrared imaging and machine learning approach
Niklas Bauriedel, Rodrigo Q. Albuquerque, Julia Utz, Nico Geis, Holger Ruckdäschel
Abstract
Abstract Additive manufacturing holds great promise for broader future use, but quality assurance and component monitoring present notable challenges. This study tackles monitoring Fused Filament Fabrication (FFF) via infrared imaging to forecast the mechanical traits of 3D‐printed items. It highlights how temperature variations, influenced by the infill's alternating orientation, affect printed parts' mechanical properties. Utilizing Machine Learning, notably the Random Forest Regressor, this research validates the capability to accurately predict tensile strength from infrared temperature readings, offering a simple, yet effective, real‐time FFF monitoring method without specialized hardware. This approach enhances the quality and dependability of 3D‐printed components with IR thermal monitoring and machine learning predictions. Highlights Infrared imaging and machine learning are combined to monitor 3D printing. A cost‐effective and accessible non‐destructive monitoring method is proposed. Temperature variation patterns of 3D printed layers influence mechanical properties.