AccScience Publishing / IJB / Volume 8 / Issue 4 / DOI: 10.18063/ijb.v8i4.620
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A Deep Learning Quality Control Loop of the Extrusion-based Bioprinting Process

Amedeo Franco Bonatti1 Giovanni Vozzi1 Chee Kai Chua2 Carmelo De Maria1*
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1 Department of Information Engineering and Research Center “Enrico Piaggio,” University of Pisa, Pisa, Italy
2 Engineering Product Development Pillar University of Technology and Design, Singapore
Submitted: 26 August 2022 | Accepted: 6 September 2022 | Published: 11 October 2022
(This article belongs to the Special Issue Related to 3D printing technology and materials)
© 2022 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( )

Extrusion-based bioprinting (EBB) represents one of the most used deposition technologies in the field of bioprinting, thanks to key advantages such as the easy-to-use hardware and the wide variety of materials that can be successfully printed. In recent years, research efforts have been focused on implementing a quality control loop for EBB, which can reduce the trial-and-error process necessary to optimize the printing parameters for a specific ink, standardize the results of a print across multiple laboratories, and so accelerate the translation of extrusion bioprinted products to more impactful clinical applications. Due to its capacity to acquire relevant features from a training dataset and generalize to unseen data, machine learning (ML) is currently being studied in literature as a relevant enabling technology for quality control in EBB. In this context, we propose a robust, deep learning-based control loop to automatically optimize the printing parameters and monitor the printing process online. We collected a comprehensive dataset of EBB prints by recording the process with a high-resolution webcam. To model multiple printing scenarios, each video represents a combination of multiple parameters, including printing set-up (either mechanical or pneumatic extrusion), material color, layer height, and infill density. After pre-processing, the collected dataset was used to thoroughly train and evaluate an ad hoc defined convolutional neural network by controlling over-fitting and optimizing the prediction time of the network. Finally, the ML model was used in a control loop to: (i) monitor the printing process and detect if a print with an error could be stopped before completion to save material and time and (ii) automatically optimize the printing parameters by combining the ML model with a previously published mathematical model of the EBB process. Altogether, we demonstrated for the first time how ML techniques can be used to automatize the EBB process, paving the way for a total quality control loop of the printing process.

Extrusion-based bioprinting
Quality control
Convolutional neuronal network
Automatic parameter optimization

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International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing