Open-Loop Control System for High Precision Extrusion-Based Bioprinting Through Machine Learning Modeling - Archive ouverte HAL
Article Dans Une Revue Journal of Machine Engineering Année : 2024

Open-Loop Control System for High Precision Extrusion-Based Bioprinting Through Machine Learning Modeling

Résumé

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. © 2024, Editorial Institution of Wrocaw Board of Scientific.
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Dates et versions

hal-04675399 , version 1 (22-08-2024)

Identifiants

Citer

Javier Arduengo, Nicolas Hascoet, Francisco Chinesta, Jean-Yves Hascoet. Open-Loop Control System for High Precision Extrusion-Based Bioprinting Through Machine Learning Modeling. Journal of Machine Engineering, 2024, 24 (1), pp.103-117. ⟨10.36897/jme/186044⟩. ⟨hal-04675399⟩
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