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¹Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, United States of America.

|February 14, 2023

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Optimizing bioink parameters using a fuzzy inference system (FIS) enhances 3D bioprinting precision and printability. This computational approach improves the accuracy and repeatability of cell-laden scaffold fabrication.

Area of Science:

Biotechnology
Biomaterials Engineering
Computational Biology

Background:

Extrusion-based 3D bioprinting is a dominant technology for creating cell-based constructs.
Bioink properties significantly impact the utility, accuracy, and precision of the bioprinting process.
Optimizing bioink-specific printing parameters is crucial for high-fidelity scaffold fabrication.

Purpose of the Study:

To investigate the use of a fuzzy inference system (FIS) as a computational method to improve 3D bioprinting precision and printability.
To identify optimal printing parameters for specific bioinks to achieve high accuracy and repeatability.
To address imprecision in 3D bioprinting test data through computational modeling.

Main Methods:

Implemented a fuzzy inference system (FIS) model with four inputs: bioink concentration, flow rate, speed, and temperature.

Keywords:

approximation extrusion bioprinting fuzzy system gelatin precision printability

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Utilized two outputs to quantify precision (scaffold bioprinted linewidth variance) and printability.

Validated the bioprinting precision index with standard and normalized printability factors.

Main Results:

Optimized printing parameters were determined using the FIS model.
High printability and precision were achieved in bioprinted scaffolds containing up to 30 × 10^6 cells/mL.
The FIS model effectively addressed imprecision in bioprinting test data.

Conclusions:

Computational methods, specifically FIS, offer a cost-efficient approach to enhance the precision and robustness of extrusion 3D bioprinting.
Optimized bioink parameters lead to improved accuracy and repeatability in scaffold fabrication.
This study demonstrates the potential of intelligent systems in advancing bioprinting technology.