Improved Modeling of Kinematics-Induced Geometric Variations in Extrusion-Based Additive Manufacturing Through Between-Printer Transfer Learning

For extrusion-based additive manufacturing, the variation in material deposition can significantly affect printed material distribution, causing infill nonuniformity and defects. These variations are induced by kinematic variations of the printer extruder. Such infill nonuniformity is more significant in an application of collaborative printing systems by which multiple printers’ extrudes co-create the same structure since more accelerate–decelerate kinematic cycles are involved. There is a lack of a quantitative understanding of the impact of printing kinematics on such variations to guide the printing process control. This article deals with the challenge by establishing a mathematical model that quantifies the printing width variations along the printing paths induced by printing speed and acceleration. The model provides vital information for predicting infill pattern nonuniformity and potentially enables using G-code adjustment to compensate for the infill errors in future research. In addition, since the model captures the mechanism of kinematics-induced variations, it provides a way of between-printer knowledge transfer on estimating printing errors. This article further proposes an informative-prior-based transfer learning algorithm to improve the quality prediction model for a printer with limited historical data by leveraging the shared data from interconnected 3-D printers. A case study based on experiments validated the effectiveness of the proposed methodology. Note to Practitioners—This article quantitatively studies the impact of extruder kinematics on geometric variations and printing quality in extrusion-based 3-D printing processes. The model can help predict the geometric printing quality and related defects, such as overfill or underfill problems given kinematics setup by G-code. This study can expedite the learning process of printing variations induced by kinematics for new printers to set up monitoring and G-code adjustment for process control in the early stage of production when the data are limited. In the long run, such between-printer transfer learning has the potential to enable the transfer learning for interconnected collaborative 3-D printing systems with improved printing efficiency and quality.