Monitoring and control of biological additive manufacturing using machine learning
Author:
Funder
National Science Foundation
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Link
https://link.springer.com/content/pdf/10.1007/s10845-023-02092-6.pdf
Reference42 articles.
1. Armstrong, A. A., Alleyne, A. G., & Wagoner Johnson, A. J. (2020). 1D and 2D error assessment and correction for extrusion-based bioprinting using process sensing and control strategies. Biofabrication, 12(4), 45023. https://doi.org/10.1088/1758-5090/aba8ee
2. Armstrong, A. A., Norato, J., Alleyne, A. G., & Wagoner Johnson, A. J. (2019). Direct process feedback in extrusion-based 3D bioprinting. Biofabrication, 12(1), 15017. https://doi.org/10.1088/1758-5090/ab4d97
3. Armstrong, A. A., Pfeil, A., Alleyne, A. G., & Wagoner Johnson, A. J. (2021). Process monitoring and control strategies in extrusion-based bioprinting to fabricate spatially graded structures. Bioprinting, 21, e00126. https://doi.org/10.1016/j.bprint.2020.e00126
4. Chen, H., Han, Q., Wang, C., Liu, Y., Chen, B., & Wang, J. (2020). Porous scaffold design for additive manufacturing in orthopedics: a review. Frontiers in Bioengineering and Biotechnology. https://doi.org/10.3389/fbioe.2020.00609
5. Cheng, Z., Cui, M., Shi, Y., Qin, Y., & Zhao, X. (2017). Fabrication of cell-laden hydrogel fibers with controllable diameters. Micromachines. https://doi.org/10.3390/mi8050161
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