Speaker
Description
In 3D extrusion bioprinting, precise and reliable material deposition is essential for fabricating consistent tissue constructs. However, elastic components like disposable syringes and flexible tubing introduce unpredictable deformation and backlash, decoupling piston movement from actual material extrusion. The challenge is further amplified with high-viscosity, non-Newtonian bioinks, whose complex shear-dependent behavior makes predictive calibration difficult. Traditional methods often rely on prior material characterization or time-consuming trial-and-error tuning, limiting flexibility across different print jobs. Additionally, residual pressure decay during pauses can further destabilize printing performance.
To overcome these challenges, we developed a two-stage automated pre-calibration procedure that integrates real-time pressure sensor data with automated imaging analysis to fine-tune print parameters without requiring prior knowledge of material properties.
In stage 1, a series of continuous lines is printed at fixed extrusion and feed rates while monitoring pressure sensor readings (Figure 1). An automated image segmentation pipeline analyzes the resulting line patterns to quantify the diameter of the extruded filaments. By correlating pressure values with the measured filament widths, we identify the pressure needed to achieve a target line width. This pressure value is then used to pre-adjust future prints, ensuring that extrusion begins at the optimal pressure, independent of initial system lag or pressure decay. Without prior pressure adjustment, the system required ~55 seconds to reach a stable pressure, during which extuded filaments were incomplete or fragmented (Figure 1, A; i). Automated analysis of the printed patterns revealed no material deposition during the first ~5 seconds, intermittent line fragments during the following ~25 seconds, and continuous extrusion only after ~45 seconds (Figure 1, A; ii, iii, iv). By contrast, when applying the pre-calibrated pressure, the system maintained stable extrusion throughout the print (Figure 1, B; i, ii). The resulting line patterns showed full and consistent deposition from the first second onward, as confirmed by quantitative image analysis of filament widths across all time intervals (Figure 1, B iii, iv).
In stage 2, lines were printed at constant extrusion rate but alternating feed rates to simulate acceleration, deceleration, and cornering. Without extrusion adjustment, only minimal and delayed pressure increases were observed in response to the changing feed rate. This caused the extruded lines to become non-uniform, wider than target width during slow segments and thinner during fast segments and led to excess material accumulation at corners. By systematically increasing the extrusion correction factor, the system began to react with stronger and more immediate pressure spikes, effectively compensating for changes in motion and yielding uniform filament widths and well-formed corners. However, beyond a certain threshold, the correction factor began to overcompensate, producing extreme pressure spikes that again disrupted line uniformity. Automated detection of line uniformity across test patterns allowed us to identify the optimal correction factor that ensured smooth, consistent lines and corners, which were then applied to print multi-layer scaffold structures.
Overall, this study shows that combining pressure sensing with automated imaging enables quick, material-independent adjustment of bioprinting parameters, improving print uniformity and reducing manual setup.