Artificial Intelligence Informed Hydrogel Biomaterials in Additive Manufacturing

Area of Science:

• Materials Science and Engineering
• Biotechnology
• Robotics

Background:

• Additive manufacturing of hydrogels is crucial for soft tissue engineering, biointerfaces, and soft robotics.
• Current limitations arise from the complex interplay between formulation, rheology, and processing conditions.

Purpose of the Study:

• To review how artificial intelligence (AI) integrates formulation, rheology, and process control for advanced hydrogel manufacturing.
• To establish a framework for predictive and reproducible hydrogel design and fabrication.

Main Methods:

• AI-driven analysis linking hydrogel composition to printability across various 3D printing techniques (DIW, inkjet, VPP, LIFT).
• Application of machine learning models for rheology prediction, droplet regime classification, and optical dose optimization.
• Utilizing polymer informatics (e.g., BigSMILES) for precursor screening and Bayesian optimization for experimental efficiency.

Main Results:

• AI models predict hydrogel strand stability from rheological data and optimize printing parameters for enhanced accuracy and viability.
• Generative screening of precursors and crosslinkers is accelerated through polymer informatics.
• Autonomous platforms with in situ sensing enable rapid iteration and closed-loop control.

Conclusions:

• AI provides quantitative pathways from hydrogel formulation to functional performance, enabling predictive manufacturing.
• This approach facilitates application-oriented design and reproducible fabrication of complex hydrogel structures.
• A strategic progression towards real-time correction and predictive fault prevention using digital twins is outlined.