Open-loop control system for high precision extrusion-based bioprinting through machine learning modelling

• Autorzy: Arduengo, Javier , Hascoet, Nicolas , Chinesta, Francisco , Hascoet, Jean-Yves
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

additive manufacturing; extrusion-based 3D bioprinting; machine learning modelling

• Czasopismo: Journal of Machine Engineering
• Rocznik: 2024
• Tom: Vol. 24, No. 1
• Strony: 103--117
• Opis fizyczny: Bibliogr. 33 poz., rys., tab.

Twórcy

autor

Arduengo, Javier

• Nantes Université, Ecole Centrale Nantes, CNRS, GeM, UMR 6183, F-44000, Nantes, France,

autor

Hascoet, Nicolas

• PIMM, Arts et Métiers Institute of Technology, CNRS - UMR 8006, F-75013, Paris, France,

autor

Chinesta, Francisco

• PIMM, Arts et Métiers Institute of Technology, CNRS - UMR 8006, F-75013, Paris, France,

autor

Hascoet, Jean-Yves

• Nantes Université, Ecole Centrale Nantes, CNRS, GeM, UMR 6183, F-44000, Nantes, France,

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Identyfikatory

DOI

10.36897/jme/186044