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Multi-material nozzle geometry design optimization for bioprinting
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AI
DOI：10.1016/j.addma.2025.104959.png)
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Abstract
En 中文
Multi-material additive manufacturing (AM) introduces complex challenges in maintaining stable and controllable flow within the printing nozzle, where flow disturbances such as backflow, excessive shear stress, and delayed material transitions can compromise print uniformity and cell viability. These issues are particularly pronounced when handling non-Newtonian, yield stress bioinks such as Herschel–Bulkley fluids. Motivated by on-the-fly material switching, we explicitly scope the optimization to a one-ink-on/one-ink-idle Y-junction in which one inlet is driven while the other remains idle. This study presents a simulation-driven optimization framework for multi-material Y-junction nozzle geometry aimed at improving backflow suppression, shear-stress minimization, and rapid material switching. A numerical model quantifies three key performance indices as backflow potential, maximum wall shear stress, and switching time index across a four dimensional design space. High fidelity CFD simulations generate training data for a Gaussian Process surrogate with a Matérn kernel, and Bayesian optimization efficiently identifies optimal geometries. The optimized designs achieve significant reductions in backflow, peak shear stress, and outlet refill time compared to baseline nozzles. Experimental validation comprising cell viability assays on high versus low shear designs, air filled backflow tests in a worst-case setup, and on-the-fly switching time measurements corroborates all three cost components. Our findings deliver a robust, scalable, and experimentally validated methodology for multi-material nozzle design, with broad implications for precision, speed, and biological functionality in extrusion-based bioprinting.
Journal
IF:
11.1
Papers:
4.6K
Citations:
4.9W
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