Open-Loop Control System for High Precision Extrusion-Based Bioprinting Through Machine Learning Modeling
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Nantes Université, École Centrale Nantes, CNRS, GeM, UMR 6183, F-44000, France
PIMM, Arts et Métiers Institute of Technology, CNRS - UMR 8006, 151 Boulevard de l’Hopital, F-75013 Paris, France
Submission date: 2024-02-01
Final revision date: 2024-03-13
Acceptance date: 2024-03-13
Online publication date: 2024-03-19
Publication date: 2024-04-02
Corresponding author
Javier Arduengo   

Nantes Université, École Centrale Nantes, CNRS, GeM, UMR 6183, F-44000, France
Journal of Machine Engineering 2024;24(1):103-117
Bioprinting is a process that uses 3D printing techniques to combine cells, growth factors, and biomaterials to create biomedical components, often with the aim of imitating natural tissue characteristics. Typically, 3D bioprinting adopts a layer-by-layer method, using materials known as bio-inks to build structures resembling tissues. This study introduces an open-loop control system designed to improve the accuracy of extrusion-based bioprinting techniques, which is composed of a specific experimental setup and a series of algorithms and models. Firstly, a method employing Logistic Regression is used to select the tests that will serve to train and test the following model. Then, using a Machine Learning Algorithm, a model that allows the optimization of printing parameters and enables process control through an open-loop system was developed. Through rigorous experimentation and validation, it is shown that the model exhibits a high degree of accuracy in independent tests. Thus, the control system offers predictability and adaptability capabilities to ensure the consistent production of high-quality bioprinted structures. Experimental results confirm the efficacy of this machine learning model and the open-loop control system in achieving optimal bioprinting outcomes.
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