A hybrid metaheuristic and computer vision approach to closed-loop calibration of fused deposition modeling 3D printers

Abstract

AbstractFused deposition modeling (FDM) is one of the most popular additive manufacturing (AM) technologies for reasons including its low cost and versatility. However, like many AM technologies, the FDM process is sensitive to changes in the feedstock material. Utilizing a new feedstock requires a time-consuming trial-and-error process to identify optimal settings for a large number of process parameters. The experience required to efficiently calibrate a printer to a new feedstock acts as a barrier to entry. To enable greater accessibility to non-expert users, this paper presents the first system for autonomous calibration of low-cost FDM 3D printers that demonstrates optimizing process parameters for printing complex 3D models with submillimeter dimensional accuracy. Autonomous calibration is achieved by combining a computer vision-based quality analysis with a single-solution metaheuristic to efficiently search the parameter space. The system requires only a consumer-grade camera and computer capable of running modern 3D printing software and uses a calibration budget of just 30 g of filament (~ $1 USD). The results show that for several popular thermoplastic filaments, the system can autonomously calibrate a 3D printer to print complex 3D models with an average deviation in dimensional accuracy of 0.047 mm, which is more accurate than the 3D printer’s published tolerance of 0.1–0.4 mm.