Physics-informed machine learning for defect identification in fused filament fabrication additive manufacturing

Fused filament fabrication (FFF) is one of the most commonly utilized 3D printing and additive manufacturing (AM) technologies. The process involves concurrently feeding and melting a thermoplastic filament material into liquefier and extruding through a nozzle in a computer-controlled print head for continuously depositing filament lines to form layers of a 3D geometry. While the deposited molten material quickly solidifies and shrinks, deposited segments and layers should fuse uniformly and bond together seamlessly. However, the mechanism in FFF process has several disadvantages such as irregular sizes of deposited filament lines, a high rate of printing errors, and poor surface finish. These defects could cause significant deformity and generate barriers for full industrial adoption of FFF processes in additive manufacturing. A method for defect identification has been proposed using physics-informed machine learning.