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International Journal of Bioprinting AI for sustainable bioprinting

3.3. Processing parameters optimization and enhanced porosity, factors critical for promoting
Besides optimizing bioink formulations, achieving cell proliferation and tissue regeneration. In vivo diabetic
consistent and high-fidelity bioprinted constructs also wound healing models showed that AI-optimized scaffolds
depends on precise control of printing parameters accelerated re-epithelialization, improved collagen
such as extrusion speed, nozzle pressure, and layer deposition, and enhanced vascularization, demonstrating
stacking accuracy. Variations in processing conditions superior therapeutic potential over conventionally printed
can significantly impact construct stability, resolution, scaffolds. This AI-assisted workflow minimizes resource
and cellular viability. 67,82,83 Traditional approaches to consumption, ensures reproducibility, and enhances the
address these challenges involve extensive trial-and- scalability of bioprinting.
error experiments to fine-tune such parameters and can 87
be time- and resource-consuming. Addressing these Fu et al. investigated the effects of printing
complexities through AI-driven optimization offers parameters on the printability of Pluronic F127 hydrogels
a transformative approach to enhancing bioprinting in extrusion-based 3D bioprinting and introduced an
efficiency and accuracy. 71,84 ML-guided optimization framework (Figure 5C). The
researchers examined the influence of nozzle temperature,
Bone et al. introduced a hierarchical ML (HML) nozzle gauge, path height, and material composition on
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framework for optimizing 3D bioprinting alginate printability, using the width index as the primary output
biopolymer (Figure 5A). The dataset included 48 alginate metric. They trained a support vector machine model on
hydrogel prints, with input parameters of ink concentration, 12 data points selected via uniform design across three key
flow rate, nozzle speed, and nozzle diameter, and output parameters—concentration, nozzle temperature, and path
targets defined as print fidelity metrics (line width and height—and generated a 3D process map that predicted
corner radius errors). The HML approach leverages domain optimal printing regions with over 75% probability of high-
knowledge, incorporating integrated system variables (e.g., fidelity output. While the study demonstrates the potential
nozzle speed, flow rate, ink concentration) with middle- of ML to reduce trial-and-error and improve parameter
layer physical relationships (e.g., effective shear rate, selection, the small dataset limits generalizability. Future
viscosity, proportionality laws) to predict and optimize work should focus on expanding the parameter space and
print fidelity. Experimental validation demonstrated dataset size, incorporating additional variables such as
that the HML framework accurately predicts optimal crosslinking conditions and cell viability, and establishing
printing parameters, achieving high-fidelity prints with standardized printability metrics to enable broader
less than 10% dimensional error. The study also highlights applicability and model reproducibility across materials
the trade-offs in optimizing specific features, such as and platforms.
linewidths and corner radii, emphasizing the need for
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multi-objective optimization. Xu et al. developed a predictive framework for
assessing cell viability in stereolithography-based
Chen et al. demonstrated an AI-driven approach 3D-bioprinted gelatin structures, addressing the
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to optimizing 3D bioprinting parameters, enhancing limitations of physics-based models through an ensemble
both efficiency and sustainability (Figure 5B). The AI- ML approach combining ridge regression, k-nearest
assisted high-throughput printing–condition–screening neighbors, random forest, and neural networks. The model
system integrates a programmable pneumatic extrusion was trained and validated on 405 cell viability data points
bioprinter with an AI-powered image-analysis algorithm, collected from 81 bioprinting conditions, using gelatin
systematically optimizing key parameters such as printing methacrylate concentration, UV intensity, UV exposure
pressure, nozzle speed, and printing distance. The model time, and layer thickness as input features. Results showed
was trained on 280 labeled images of alginate–gelatin that UV exposure time had the greatest impact on cell
hydrogel prints, using deep learning to classify extrusion viability, followed by layer thickness, gelatin methacrylate
states and predict optimal print conditions. By automating concentration, and UV intensity.
the screening process, the AI-assisted high-throughput
printing–condition–screening system eliminates reliance Zhang et al. developed an integrated framework
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on extensive trial-and-error experimentation, reducing combining advanced rheological modeling, computational
material waste while ensuring high-quality scaffold fluid dynamics simulations, and ML to predict as-extruded
fabrication. Experimental results demonstrated that the AI- cell viability in extrusion-based 3D bioprinting. The study
assisted approach led to a 45% reduction in optimization used support vector regression to predict Cross power
time, significantly lowering the consumption of bioinks law parameters for alginate inks based on 76 rheological
and resources. The optimized hydrogel scaffolds exhibited measurements across different concentrations and
improved mechanical stability, uniform fiber alignment, temperatures, and trained multilayer perceptron regressors

Volume 11 Issue 4 (2025) 141 doi: 10.36922/IJB025170164