Predict process conditions before printing
A 2026 Bioprinting study introduced a mathematical framework that predicts extrusion pressure directly from rheological data and uses the predicted pressure to estimate wall shear stress before printing. Across multiple nozzle types, the model achieved reported relative errors of approximately 5–15% compared with experimentally adjusted pressures while printed constructs maintained more than 80% cell viability.
- Rheology-derived extrusion-pressure prediction.
- Pressure-based printability windows.
- Pre-print estimation of wall shear stress.
- A route to reducing empirical trial-and-error during parameter development.
Optimise the nozzle and the material together
A separate 2026 study involving Brinter AM Technologies combined computational fluid dynamics and response surface methodology to optimise nozzle geometry together with biomaterial rheology. Rather than treating the printhead and material as independent variables, the work explored them as one coupled process-design problem.
- Joint optimisation of nozzle geometry and material rheology.
- CFD-based exploration of the extrusion design space.
- Multi-objective optimisation balancing flow performance and wall shear stress.
- Data-driven classification of high-performing nozzle–material combinations.
A stronger foundation for advanced process control
These studies illustrate a broader engineering direction that is central to advanced additive manufacturing: moving process development from manual parameter iteration toward models that connect material behaviour, tooling geometry and manufacturing conditions. Predictive models can support faster development, safer processing windows and a stronger basis for later sensing, control, validation and digital-twin integration.