Machine Learning-Based Prediction of Bioprinting Quality Using Integrated Experimental and Computational Data

Three-dimensional (3D) bioprinting of hydrogel inks is an enabling technology for tissue engineering and the fabrication of personalized drug-delivery systems. However, in most laboratories the extrusion parameters are still optimized by slow, resource-intensive trial-and-error, because printability depends on the combined and often nonlinear influence of ink composition, extrusion pressure, and nozzle geometry rather than on any single variable. Alginate-methylcellulose (Alg-MC) is a widely used pharmaceutical bioink whose shear-thinning behavior makes this optimization particularly demanding. Here we developed a physics-informed machine-learning framework that predicts extrusion print quality before printing. A hybrid dataset of 3280 labeled samples was assembled by combining laboratory extrusion experiments, computational fluid dynamics (CFD) simulations of nozzle flow, and controlled augmentation with a deep belief network (DBN). Five physics-informed features encoding pressure-to-diameter and shear-rate interactions were engineered to embed rheological domain knowledge. Five base classifiers alongside two ensemble strategies, seven models in all, were benchmarked under five-fold stratified cross-validation. The voting ensemble performed best (accuracy, 93.3%; F1 = 0.929), narrowly ahead of the stacking ensemble (93.3% accuracy; F1 = 0.928). Alg:MC ratio, extrusion pressure, and the pressure-diameter interaction term stood out as the dominant predictors, consistent with established alginate rheology. On external validation-withholding one composition ratio and one needle gauge entirely from training and testing on those genuinely unseen conditions-accuracy was 75.9%, a stricter test than a random held-out split. Overall, the framework offers a practical, interpretable way to cut down experimental burden in pharmaceutical bioink development.