A Simple Rheology‐Based Framework for Selecting Printing Parameters in Extrusion‐Based Hydrogel Additive Manufacturing

Abstract

Predicting whether a gel formulation will print successfully in extrusion‐based additive manufacturing (direct ink writing, DIW) remains slow and resource‐intensive: computational fluid dynamics requires meshing and solver expertise, and machine‐learning methods depend on large training datasets. We present a fast, open, modular analytical framework that converts standard rheological measurements directly into printing parameters, so printability can be assessed before experimental optimization. Using a steady‐shear flow curve (power‐law and cross fits), the geometry of a syringe‐based DIW printer, and a candidate deposition speed, the framework computes the complete extrusion field in closed form within seconds: pressure contributions from syringe, needle, and hydrostatic head; Rabinowitsch‐corrected wall shear rate and stress; the generalized Reynolds number with laminar‐flow verification; the radial velocity profile; and slicer‐ready settings including a calibratable extrusion multiplier, . Oscillatory and recovery measurements supply complementary shape‐stability and deposition‐fidelity criteria. Exercised on citric‐acid‐crosslinked carboxymethyl cellulose hydrogels (C10–C25) and a chemically unrelated commercial gel, every formulation printed inside its predicted operational window, and the analysis resolved how the highly structured C20 formulation prints with high fidelity despite limited thixotropic recovery. The framework offers a physically grounded pre‐screen that complements CFD and machine learning while reducing experimental effort.