Open-Loop Control System for High Precision Extrusion-Based Bioprinting Through Machine Learning Modeling - Journal of Machine Engineering - Vol. 24 No. 1

Aug 4, 2026

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Bioprinting forms engineered tissues by depositing biomaterials and living cells layer by layer, yet extrusion-based printing suffers from complex, nonlinear links between pressure, rheology, filament geometry, and layer-height settings. This study presents an open-loop control system for high-precision extrusion-based bioprinting, combining Logistic Regression for selecting training and test experiments with a machine-learning model that optimizes printing parameters and drives predictive, adaptable process control. Results from rigorous experimentation and independent validation confirm high accuracy and demonstrate reliable production of high-quality bioprinted structures.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Abstract/Overview

Bioprinting process and extrusion-based technologies

Proposed open-loop control system

Logistic Regression for dataset selection

Machine-learning model for parameter optimization

Experimental validation and accuracy results

Science Arts & Métiers (SAM) is an open access repository that collects the work of Arts et Métiers Institute of Technology researchers and makes it freely available over the web where possible. This is an author-deposited version published in: [https://sam.ensam.eu](https://sam.ensam.eu)[ ](https://sam.ensam.eu)Handle ID: .[http://hdl.handle.net/10985/25490](http://hdl.handle.net/10985/25490) This document is available under CC BY-NC license To cite this version : Javier ARDUENGO, Nicolas HASCOËT, Francisco CHINESTA SORIA, Jean-Yves HASCOETOpen-Loop Control System for High Precision Extrusion-Based Bioprinting Through Machine Learning Modeling-Journal of Machine Engineering-Vol. 24, n°1, p.103-117-2024 | Any correspondence concerning this service should be sent to the repository [Administrator : ](Administrator : scienceouverte@ensam.eu)[scienceouverte@ensam.eu](Administrator : scienceouverte@ensam.eu) | | | --- | --- | Received: 01 February 2024 / Accepted: 13 March 2024 / Published online: 19 March 2024 additive manufacturing, extrusion-based 3D bioprinting, machine learning modelling Javier ARDUENGO1*, Nicolas HASCOET2 , Francisco CHINESTA2 , Jean-Yves HASCOET 1 OPEN-LOOP CONTROL SYSTEM FOR HIGH PRECISION EXTRUSION-BASED BIOPRINTING THROUGH MACHINE LEARNING MODELLING 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, 3Dbioprinting 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. 1. INTRODUCTION 3D Bioprinting, also known as Additive Biomanufacturing, is a process that involves positioning biomaterials and living cells layer by layer to create engineered tissues and organs in 3D, preserving cellular viability [1] . Bioinks, used in bioprinting, consist of natural or synthetic biomaterials mixed with living cells. They come in two main types: cell-based bioinks with live cells alone or hydrogel-based bioinks combined with cell-laden natural, synthetic, or decellularized tissue hydrogels. Coupled with post-processing to ensure thematuring of the living construct, the potential applications for bioprinting includes [2]: in vitro 3D tissue/organ models for drug screening, organ development, toxicological, cosmetic research, etc., and tailoring of living structures for clinical transplantation or tissue repair. 1 Nantes Université, Ecole Centrale Nantes, CNRS, GeM, UMR 6183, F-44000, Nantes, France 2 PIMM, Arts et Métiers Institute of Technology, CNRS-UMR 8006, F-75013, Paris, France * [E-mail: javier.arduengo-garcia@ec-nantes.fr](E-mail: javier.arduengo-garcia@ec-nantes.fr), [nicolas.hascoet@ensam.eu](nicolas.hascoet@ensam.eu), [franciso.chinesta@ensam.eu](franciso.chinesta@ensam.eu), [jean-yves.hascoet@ec-nantes.fr](jean-yves.hascoet@ec-nantes.fr)[ ](jean-yves.hascoet@ec-nantes.fr)[https://doi.org/10.36897/jme/186044](https://doi.org/10.368

FAQs

What problem does the proposed open-loop control system address in extrusion-based bioprinting?

It addresses the difficulty of achieving geometrically accurate constructs caused by the complex relationship between input pressure, material rheology, and the resulting filament size and shape across layers.

How is Logistic Regression used in the study?

Logistic Regression is used to select the experiments that form the training and testing sets for the subsequent machine-learning model.

What evidence supports that the machine-learning model and control system work effectively?

Rigorous experimentation and independent testing show the model achieves a high degree of accuracy, confirming effective optimization of bioprinting outcomes.

Aug 4, 2026

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