Page 263 - IJB-9-3
P. 263
International Journal of Bioprinting Multi-material bioprinting with OCT imaging
21. Busra TD, Fatma BE, Tugba A, et al., 2019, 3D bio-printing 31. Gerdes S, Mostafavi A, Ramesh S, et al., 2020, Process-
of levan/polycaprolactone/gelatin blends for bone tissue structure-quality relationships of 3D printed PCL-
engineering: Characterization of the cellular behavior. hydroxyapatite scaffolds. Tissue Eng Part A, 26(5-6):279–291.
Polym Paint Colour, 119:426–437.
https://doi.org/10.1089/ten.TEA.2019.0237
https://doi.org/10.1016/j.eurpolymj.2019.08.015
32. Joshua WT, Daniel JS, Brian C, et al., 2022, In situ volumetric
22. Chen Y, Xiong X, Liu X, et al., 2020, Bioprinting of shear- imaging and analysis of FRESH 3D bioprinted constructs
thinning hybrid bioinks with excellent bioactivity derived using optical coherence tomography. Biofabrication,
from gellan/alginate and thixotropic magnesium phosphate- 15(1):104102.
based gels. J Mater Chem B, 8:5500–5514.
https://doi.org/10.1088/1758-5090/ac975e
https://doi.org/10.1039/D0TB00060D
33. Yang S, Wang L, Chen Q, et al., 2021, In situ process
23. Shao Y, Han R, Quan X, et al., 2021, Study on ink flow of monitoring and automated multi-parameter evaluation
silicone rubber for direct ink writing. J Appl Polym Sci, using optical coherence tomography during extrusion-
138(33):50819. based bioprinting. Addit Manuf, 47:102251.
https://doi.org/10.1002/app.50819 https://doi.org/10.1016/j.addma.2021.102251
24. Peki A, Ekici B, 2021, Experimental and statistical analysis of 34. Yang S, Chen Q, Wang L, et al., 2022, In situ defect detection
robotic 3D printing process parameters for continuous fiber and feedback control with three-dimensional extrusion-
reinforced composites. Int J Compos Mater, 55(19):2645–2655. based bioprinter-associated optical coherence tomography.
Int J Bioprint, 9(1):642.
https://doi.org/10.1177/0021998321996425
https://doi.org/10.18063/ijb.v9i1.624
25. Zhou L, Gao Q, Fu J, 2019, Multi-material 3D printing
of highly stretchable silicone elastomer. ACS Appl Mater 35. Geng P, Zhao J, Wu W, et al., 2019, Effects of extrusion speed
Interfaces, 11(26):23573–23583. and printing speed on the 3D printing stability of extruded
PEEK filament. J Manuf Process, 37:266–273.
https://doi.org/10.1021/acsami.9b04873
https://doi.org/10.1016/j.jmapro.2018.11.023
26. Nicholas B, Chen XB, 2022, Review of extrusion-based
multi-material bioprinting processes—ScienceDirect. 36. Jeffrey P, Tian X, Albert S, 2018, Measurement and modeling
Bioprinting, 25:e00189. of forces in extrusion-based additive manufacturing of
https://doi.org/10.1016/j.bprint.2021.e00189 flexible silicone elastomer with thin wall structures. J Manuf
Sci Eng, 140(9):09100.
27. Hoelzle DJ, Alleyne AG, Johnson A, 2008, Iterative learning
control for robotic deposition using machine vision. https://doi.org/10.1115/1.4040350
American Control Conference, 2008. 37. Liu C, Liu J, Yang C, et al., 2022, Computer vision-aided 2D
https://doi.org/10.1109/ACC.2008.4587211 error assessment and correction for helix bioprinting. Int J
Bioprint, 8(2):547.
28. Armstrong AA, Norato J, Andrew GA, et al., 2020, Direct
process feedback in extrusion-based 3D bioprinting. https://doi.org/10.18063/ijb.v8i2.547
Biofabrication, 12(1):015017. 38. Braeden W, Barry JD, 2017, Parameter optimization for 3D
bioprinting of hydrogels. Bioprinting, 8:8–12.
https://doi.org/10.1088/1758-5090/ab4d97
https://doi.org/10.1016/j.bprint.2017.09.001
29. Armstrong AA, Alleyne AG, Johnson A, 2020, 1D and
2D error assessment and correction for extrusion-based 39. Wei LN, Alvin C, Yew SO, et al., 2022, Deep learning for
bioprinting using process sensing and control strategies. fabrication and maturation of 3D bioprinted tissues and
Biofabrication, 12(4):045023. organs. Virtual Phys Prototyp, 15(11):1–19.
https://doi.org/10.1088/1758-5090/aba8ee https://doi.org/10.1080/17452759.2020.1771741
30. Almela T, Brook IM, Khoshroo K, et al., 2017, Simulation of 40. Bonatti AF, Vozzi G, Chua CK, et al., 2022, A deep learning
cortico-cancellous bone structure by 3D printing of bilayer quality control loop of the extrusion based bioprinting
calcium phosphate-based scaffolds. Bioprinting, 6:1–7. process. Int J Bioprint, 8(4):620.
https://doi.org/10.1016/j.bprint.2017.04.001 https://doi.org/10.18063/ijb.v8i4.620
Volume 9 Issue 3 (2023) 255 https://doi.org/10.18063/ijb.707

