ArticleInternational journal of bioprinting2023
Continuous and highly accurate multi-material extrusion-based bioprinting with optical coherence tomography imaging.
Article in International journal of bioprinting, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- A Critical Review: Gel-Based Edible Inks for 3D Food Printing: Materials, Rheology-Geometry Mapping, and Control.Gels (Basel, Switzerland) · 2025Review
- Extrusion bioprinting: meeting the promise of human tissue biofabrication?Progress in biomedical engineering (Bristol, England) · 2025Review
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Material Extrusion Filament Width and Height Prediction via Design of Experiment and Machine Learning.Micromachines · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extrusion-based bioprinting is a widely used approach to construct artificial organs or tissues in the medical fields due to its easy operation and good ability to combine multimaterial. Nevertheless, the current technology is limited to some printing errors when combining multi-material printing, including mismatch between printing filaments of different materials and error deposited materials (e.g., under-extrusion and overextrusion). These errors will affect the function of the printed structure (e.g., mechanical and biological properties), and the traditional manual correction methods are inefficient in time and material, so an automatic procedure is needed to improve multimaterial printing accuracy and efficiency. However, to the best of our knowledge, very few automated procedure can achieve the registration between printing filaments of different materials. Herein, we utilized optical coherence tomography (OCT) to monitor printing process and presented a multi-material static model and a time-related control model in extrusion-based multi-material bioprinting. Specifically, the multi-material static model revealed the relationship between printed filament metrics (filament size and layer thickness) and printing parameters (printing speeds or pressures) with different materials, which enables the registration of printing filaments by rapid selection of printing parameters for the materials, while time-related control model could correct control parameters of nozzles to reduce the material deposition error at connection point between nozzles in a short time. According to the experimental results of singlelayer scaffold and multi-layer scaffold, material deposition error is eliminated, and the same layer thickness between different materials of the same layer is achieved, which proves the accuracy and practicability of these models. The proposed models could achieve improved precision of printed structure and printing efficiency.
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