Abstract
The goal of this work is the flaw-free, industrial-scale production of biological additive manufacturing of tissue constructs (Bio-AM). In pursuit of this goal, the objectives of this work in the context of extrusion-based Bio-AM of bone tissue constructs are twofold: (1) detect flaw formation using data from in-situ infrared thermocouple sensors; and (2) prevent flaw formation through preemptive process control. In realizing the first objective, data signatures acquired from in-situ sensors were analyzed using several machine learning approaches to ascertain critical quality metrics, such as print regime, strand width, strand height, and strand fusion severity. These quality metrics are intended to capture the process state at the basic 1D strand-level to the 2D layer-level. For this purpose, machine learning models were trained to classify and predict flaw formation. These models predicted print quality features with accuracy nearing 90%. In connection with the second objective, the previously trained machine learning models were used to preempt flaw formation by changing the process parameters (print velocity) during deposition—a form of feedforward control. With the feedforward process control, strand width heterogeneity was statistically significantly reduced, reducing the strand width difference between strand halves to less than 50 µm. Using this integrated process monitoring, detection, and control approach, we demonstrate consistent, repeatable production of Bio-AM constructs.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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
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
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
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
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
Derakhshanfar, S., Mbeleck, R., Xu, K., Zhang, X., Zhong, W., & Xing, M. (2018). 3D bioprinting for biomedical devices and tissue engineering: a review of recent trends and advances. Bioactive Materials, 3(2), 144–156. https://doi.org/10.1016/j.bioactmat.2017.11.008
Gaikwad, A., Giera, B., Guss, G. M., Forien, J. B., Matthews, M. J., & Rao, P. (2020). Heterogeneous sensing and scientific machine learning for quality assurance in laser powder bed fusion—a single-track study. Additive Manufacturing, 36, 101659. https://doi.org/10.1016/j.addma.2020.101659
Gerdes, S., Mostafavi, A., Ramesh, S., Memic, A., Rivero, I. V., Rao, P., & Tamayol, A. (2020). Process–structure–quality relationships of three-dimensional printed poly(caprolactone)-hydroxyapatite scaffolds. Tissue Engineering Part A, 26(5–6), 279–291. https://doi.org/10.1089/ten.tea.2019.0237
Gerdes, S., Ramesh, S., Mostafavi, A., Tamayol, A., Rivero, I. V., & Rao, P. (2021). Extrusion-based 3D (Bio)printed tissue engineering scaffolds: process–structure–quality relationships. ACS Biomaterials Science & Engineering, 7(10), 4694–4717. https://doi.org/10.1021/acsbiomaterials.1c00598
Habib, A., & Khoda, B. (2019). Development of clay based novel hybrid bio-ink for 3D bio-printing process. Journal of Manufacturing Processes, 38, 76–87. https://doi.org/10.1016/j.jmapro.2018.12.034
Habib, A., Sathish, V., Mallik, S., & Khoda, B. (2018). 3D printability of alginate-carboxymethyl cellulose hydrogel. Materials, 11, 454. https://doi.org/10.3390/ma11030454
Haglund, L., Ahangar, P., & Rosenzweig, D. H. (2019). Advancements in 3D printed scaffolds to mimic matrix complexities for musculoskeletal repair. Current Opinion in Biomedical Engineering, 10, 142–148. https://doi.org/10.1016/j.cobme.2019.06.002
He, Y., Yang, F., Zhao, H., Gao, Q., Xia, B., & Fu, J. (2016). Research on the printability of hydrogels in 3D bioprinting. Scientific Reports, 6(1), 29977. https://doi.org/10.1038/srep29977
Heinrich, M. A., Liu, W., Jimenez, A., Yang, J., Akpek, A., Liu, X., et al. (2019). 3D bioprinting: from benches to translational applications. Small (weinheim an Der Bergstrasse, Germany), 15(23), 1805510. https://doi.org/10.1002/smll.201805510
Hölzl, K., Lin, S., Tytgat, L., van Vlierberghe, S., Gu, L., & Ovsianikov, A. (2016). Bioink properties before, during and after 3D bioprinting. Biofabrication, 8(3), 32002. https://doi.org/10.1088/1758-5090/8/3/032002
Hulbert, S. F., Young, F. A., Mathews, R. S., Klawitter, J. J., Talbert, C. D., & Stelling, F. H. (1970). Potential of ceramic materials as permanently implantable skeletal prostheses. Journal of Biomedical Materials Research, 4(3), 433–456. https://doi.org/10.1002/jbm.820040309
Jenkins, T. L., & Little, D. (2019). Synthetic scaffolds for musculoskeletal tissue engineering: cellular responses to fiber parameters. Npj Regenerative Medicine, 4(1), 15. https://doi.org/10.1038/s41536-019-0076-5
Jin, Z., Zhang, Z., Shao, X., & Gu, G. X. (2021). Monitoring anomalies in 3D bioprinting with deep neural networks. ACS Biomaterials Science & Engineering. https://doi.org/10.1021/acsbiomaterials.0c01761
Klawitter, J. J., Bagwell, J. G., Weinstein, A. M., Sauer, B. W., & Pruitt, J. R. (1976). An evaluation of bone growth into porous high density polyethylene. Journal of Biomedical Materials Research, 10(2), 311–323. https://doi.org/10.1002/jbm.820100212
Kuboki, Y., Jin, Q., & Takita, H. (2001). Geometry of carriers controlling phenotypic expression in BMP-induced osteogenesis and chondrogenesis. JBJS. https://doi.org/10.2106/00004623-200100002-00005
Lane, B., & Whitenton, E. (2016). Calibration and measurement procedures for a high magnification thermal camera. National Institute of Standards and Technology.
Lee, S. J., Lee, I. W., Lee, Y. M., Lee, H. B., & Khang, G. (2004). Macroporous biodegradable natural/synthetic hybrid scaffolds as small intestine submucosa impregnated poly(D, L-lactide-co-glycolide) for tissue-engineered bone. Journal of Biomaterials Science, Polymer Edition, 15(8), 1003–1017. https://doi.org/10.1163/1568562041526487
Mostafavi, A., Abudula, T., Russell, C. S., Mostafavi, E., Williams, T. J., Salah, N., et al. (2021a). In situ printing of scaffolds for reconstruction of bone defects. Acta Biomaterialia, 127, 313–326. https://doi.org/10.1016/j.actbio.2021.03.009
Mostafavi, A., Samandari, M., Karvar, M., Ghovvati, M., Endo, Y., Sinha, I., et al. (2021b). Colloidal multiscale porous adhesive (bio)inks facilitate scaffold integration. Applied Physics Reviews, 8(4), 041415. https://doi.org/10.1063/5.0062823
Murphy, C. M., & O’Brien, F. J. (2010). Understanding the effect of mean pore size on cell activity in collagen-glycosaminoglycan scaffolds. Cell Adhesion & Migration, 4(3), 377–381. https://doi.org/10.4161/cam.4.3.11747
Nehrer, S., Breinan, H. A., Ramappa, A., Young, G., Shortkroff, S., Louie, L. K., et al. (1997). Matrix collagen type and pore size influence behaviour of seeded canine chondrocytes. Biomaterials, 18(11), 769–776. https://doi.org/10.1016/S0142-9612(97)00001-X
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: machine learning in python. Journal of Machine Learning Research, 12(85), 2825–2830.
Ramesh, S., Harrysson, O. L. A., Rao, P. K., Tamayol, A., Cormier, D. R., Zhang, Y., & Rivero, I. V. (2021). Extrusion bioprinting: Recent progress, challenges, and future opportunities. Bioprinting, 21, e00116. https://doi.org/10.1016/j.bprint.2020.e00116
Rao, P. K., Liu, J., Roberson, D., Kong, Z., & Williams, C. (2015). Online real-time quality monitoring in additive manufacturing processes using heterogeneous sensors. Journal of Manufacturing Science and Engineering. https://doi.org/10.1115/1.4029823
Ribeiro, A., Blokzijl, M. M., Levato, R., Visser, C. W., Castilho, M., Hennink, W. E., et al. (2017). Assessing bioink shape fidelity to aid material development in 3D bioprinting. Biofabrication, 10(1), 14102. https://doi.org/10.1088/1758-5090/aa90e2
Roosa, S. M. M., Kemppainen, J. M., Moffitt, E. N., Krebsbach, P. H., & Hollister, S. J. (2010). The pore size of polycaprolactone scaffolds has limited influence on bone regeneration in an in vivo model. Journal of Biomedical Materials Research Part A, 92A(1), 359–368. https://doi.org/10.1002/jbm.a.32381
Roy, M., Yavari, R., Zhou, C., Wodo, O., & Rao, P. (2019). Prediction and experimental validation of part thermal history in the fused filament fabrication additive manufacturing process. Journal of Manufacturing Science and Engineering. https://doi.org/10.1115/1.4045056
Samandari, M., Quint, J., Rodríguez-delaRosa, A., Sinha, I., Pourquié, O., & Tamayol, A. (2021). Bioinks and bioprinting strategies for skeletal muscle tissue engineering. Advanced Materials, 12, 2105883. https://doi.org/10.1002/adma.202105883
Scime, L., & Beuth, J. (2019). Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Additive Manufacturing, 25, 151–165. https://doi.org/10.1016/j.addma.2018.11.010
Soltan, N., Ning, L., Mohabatpour, F., Papagerakis, P., & Chen, X. (2019). Printability and cell viability in bioprinting alginate dialdehyde-gelatin scaffolds. ACS Biomaterials Science & Engineering, 5(6), 2976–2987. https://doi.org/10.1021/acsbiomaterials.9b00167
Thattaruparambil Raveendran, N., Vaquette, C., Meinert, C., Samuel Ipe, D., & Ivanovski, S. (2019). Optimization of 3D bioprinting of periodontal ligament cells. Dental Materials, 35(12), 1683–1694. https://doi.org/10.1016/j.dental.2019.08.114
Tsuruga, E., Takita, H., Itoh, H., Wakisaka, Y., & Kuboki, Y. (1997). Pore size of porous hydroxyapatite as the cell-substratum controls BMP-induced osteogenesis1. The Journal of Biochemistry, 121(2), 317–324. https://doi.org/10.1093/oxfordjournals.jbchem.a021589
Webb, B., & Doyle, B. J. (2017). Parameter optimization for 3D bioprinting of hydrogels. Bioprinting, 8, 8–12. https://doi.org/10.1016/j.bprint.2017.09.001
Williams, J. M., Adewunmi, A., Schek, R. M., Flanagan, C. L., Krebsbach, P. H., Feinberg, S. E., et al. (2005). Bone tissue engineering using polycaprolactone scaffolds fabricated via selective laser sintering. Biomaterials, 26(23), 4817–4827. https://doi.org/10.1016/j.biomaterials.2004.11.057
Yang, S., Wang, L., Chen, Q., & Xu, M. (2021). In situ process monitoring and automated multi-parameter evaluation using optical coherence tomography during extrusion-based bioprinting. Additive Manufacturing, 47, 102251. https://doi.org/10.1016/j.addma.2021.102251
Yu, C., & Jiang, J. (2020). A perspective on using machine learning in 3D bioprinting. International Journal of Bioprinting, 6(1), 253. https://doi.org/10.18063/ijb.v6i1.253
Zehnder, T., Sarker, B., Boccaccini, A. R., & Detsch, R. (2015). Evaluation of an alginate–gelatine crosslinked hydrogel for bioplotting. Biofabrication, 7(2), 025001. https://doi.org/10.1088/1758-5090/7/2/025001
Acknowledgements
Financial support from the National Science Foundation (NSF CMMI-1719388, CMMI-1739696, and CMMI-1752069), the University of Nebraska-Lincoln, and Nebraska Tobacco Settlement Biomedical Research Enhancement Funds are gratefully acknowledged. Specifically, the concept of in-situ sensing for process monitoring and assessing the effect of process conditions on quality of deposits in 3D (bio)printing of biomaterials was funded through CMMI-1739696 (Program Officer: Dr. Bruce Kramer). The foregoing grant provided funding for Dr. Samuel Gerdes’ Ph.D. studies, and travel to academic conferences.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
No competing financial interests exist in this research.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gerdes, S., Gaikwad, A., Ramesh, S. et al. Monitoring and control of biological additive manufacturing using machine learning. J Intell Manuf 35, 1055–1077 (2024). https://doi.org/10.1007/s10845-023-02092-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-023-02092-6