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Published Online: 16 December 2020

Machine Learning-Guided Three-Dimensional Printing of Tissue Engineering Scaffolds

Publication: Tissue Engineering Part A
Volume 26, Issue Number 23-24

Abstract

Various material compositions have been successfully used in 3D printing with promising applications as scaffolds in tissue engineering. However, identifying suitable printing conditions for new materials requires extensive experimentation in a time and resource-demanding process. This study investigates the use of Machine Learning (ML) for distinguishing between printing configurations that are likely to result in low-quality prints and printing configurations that are more promising as a first step toward the development of a recommendation system for identifying suitable printing conditions. The ML-based framework takes as input the printing conditions regarding the material composition and the printing parameters and predicts the quality of the resulting print as either “low” or “high.” We investigate two ML-based approaches: a direct classification-based approach that trains a classifier to distinguish between low- and high-quality prints and an indirect approach that uses a regression ML model that approximates the values of a printing quality metric. Both modes are built upon Random Forests. We trained and evaluated the models on a dataset that was generated in a previous study, which investigated fabrication of porous polymer scaffolds by means of extrusion-based 3D printing with a full-factorial design. Our results show that both models were able to correctly label the majority of the tested configurations while a simpler linear ML model was not effective. Additionally, our analysis showed that a full factorial design for data collection can lead to redundancies in the data, in the context of ML, and we propose a more efficient data collection strategy.

Abstract

Impact statement

This study investigates the use of Machine Learning (ML) for predicting the printing quality given the printing conditions in extrusion-based 3D printing of biomaterials. Classification and regression methods built upon Random Forests show promise for the development of a recommendation system for identifying suitable printing conditions reducing the amount of required experimentation. This study also gives insights on developing an efficient strategy for collecting data for training ML models for predicting printing quality in extrusion-based 3D printing of biomaterials.

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References

1. Lee, J.Y., An, J., and Chua, C.K. Fundamentals and applications of 3D printing for novel materials. Appl Mater Today 7, 120, 2017.
2. Koons, G.L., Diba, M., and Mikos, A.G. Materials design for bone-tissue engineering. Nat Rev Mater 5, 584, 2020.
3. Bittner, S.M., Guo, J.L., Melchiorri, A., and Mikos, A.G. Three-dimensional printing of multilayered tissue engineering scaffolds. Mater Today 21, 861, 2018.
4. Roopavath, U.K., Malferrari, S., Van Haver, A., Verstreken, F., Rath, S.N., and Kalaskar, D.M. Optimization of extrusion based ceramic 3D printing process for complex bony designs. Mater Des 162, 263, 2019.
5. Kyle, S., Jessop, Z.M., Al-Sabah, A., and Whitaker, I.S. ‘Printability’ of candidate biomaterials for extrusion based 3D printing: state-of-the-art. Adv Healthc Mater 6, 1700264, 2017.
6. He, Y., Yang, F., Zhao, H., Gao, Q., Xia, B., and Fu, J. Research on the printability of hydrogels in 3D bioprinting. Sci Rep 6, 29977, 2016.
7. Paxton, N., Smolan, W., Böck, T., Melchels, F., Groll, J., and Jungst, T. Proposal to assess printability of bioinks for extrusion-based bioprinting and evaluation of rheological properties governing bioprintability. Biofabrication 9, 044107, 2017.
8. Gao, T., Gillispie, G.J., Copus, J.S., et al. Optimization of gelatin-alginate composite bioink printability using rheological parameters: a systematic approach. Biofabrication 10, 034106, 2018.
9. Jin, Y., Chai, W., and Huang, Y. Printability study of hydrogel solution extrusion in nanoclay yield-stress bath during printing-then-gelation biofabrication. Mater Sci Eng C 80, 313, 2017.
10. Diamantides, N., Wang, L., Pruiksma, T., et al. Correlating rheological properties and printability of collagen bioinks: the effects of riboflavin photocrosslinking and pH. Biofabrication 9, 034102, 2017.
11. Murphy, S.V., Skardal, A., and Atala, A. Evaluation of hydrogels for bio-printing applications. J Biomed Mater Res Part A 101 A, 272, 2013.
12. Trachtenberg, J.E., Placone, J.K., Smith, B.T., et al. Extrusion-based 3d printing of poly(propylene fumarate) in a full-factorial design. ACS Biomater Sci Eng 2, 1771, 2016.
13. Menon, A., Póczos, B., Feinberg, A.W., and Washburn, N.R. Optimization of silicone 3d printing with hierarchical machine learning. 3D Print Addit Manuf 6, 181, 2019.
14. Jin, Z., Zhang, Z., and Gu, G.X. Automated real-time detection and prediction of interlayer imperfections in additive manufacturing processes using artificial intelligence. Adv Intell Syst 2, 1900130, 2020.
15. Jin, Z., Zhang, Z., and Gu, G.X. Autonomous in-situ correction of fused deposition modeling printers using computer vision and deep learning. Manuf Lett 22, 11, 2019.
16. Gu, G.X., Chen, C.T., Richmond, D.J., and Buehler, M.J. Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment. Mater Horizons 5, 939, 2018.
17. Abueidda, D.W., Almasri, M., Ammourah, R., Ravaioli, U., Jasiuk, I.M., and Sobh, N.A. Prediction and optimization of mechanical properties of composites using convolutional neural networks. Compos Struct 227, 111264, 2019.
18. Gu, G.X., Chen, C.T., and Buehler, M.J. De novo composite design based on machine learning algorithm. Extrem Mech Lett 18, 19, 2018.
19. Silbernagel, C., Aremu, A., and Ashcroft, I. Using machine learning to aid in the parameter optimisation process for metal-based additive manufacturing. Rapid Prototyp J 26, 625, 2019.
20. Després, N., Cyr, E., Setoodeh, P., and Mohammadi, M. Deep learning and design for additive manufacturing: a framework for microlattice architecture. JOM 72, 2408, 2020.
21. Zhang, Z., Poudel, L., Sha, Z., Zhou, W., and Wu, D. Data-driven predictive modeling of tensile behavior of parts fabricated by cooperative 3d printing. J Comput Inf Sci Eng 20, JCISE-19-1204, 2020.
22. Herriott, C., and Spear, A.D. Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods. Comput Mater Sci 175, 109599, 2020.
23. Lee, J., Oh, S.J., An, S.H., Kim, W.-D., and Kim, S.-H. Machine learning-based design strategy for 3D printable bioink: elastic modulus and yield stress determine printability. Biofabrication 12, 035018, 2020.
24. Guo, Y., Lu, W.F., and Fuh, J.Y.H. Semi-supervised deep learning based framework for assessing manufacturability of cellular structures in direct metal laser sintering process. J Intell Manuf 2020 [Epub ahead of print];.
25. Akhil, V., Raghav, G., Arunachalam, N., and Srinivas, D.S. Image data-based surface texture characterization and prediction using machine learning approaches for additive manufacturing. J Comput Inf Sci Eng 20, JCISE-19-1222, 2020.
26. Kunkel, M.H., Gebhardt, A., Mpofu, K., and Kallweit, S. Quality assurance in metal powder bed fusion via deep-learning-based image classification. Rapid Prototyp J 26, 259, 2019.
27. Gardner, J.M., Hunt, K.A., Ebel, A.B., et al. Machines as craftsmen: localized parameter setting optimization for fused filament fabrication 3D printing. Adv Mater Technol 4, 1800653, 2019.
28. Breiman, L. Random forests. Mach Learn 45, 5, 2001.
29. Bishop, C.M. Linear models for regression. In: Jordan, M., Kleinberg, J., and Scholkopf, B., eds. Pattern Recognit Mach Learn. New York, NY: Springer Science and Business Media LLC, 2006, pp. 137–179.
30. Fabian, P., Varoquaux, G., Michel, V., et al. Scikit-learn: machine learning in python. J Mach Learn Res 12, 2825, 2011.
31. Fawcett, T. An introduction to ROC analysis. Pattern Recognit Lett 27, 861, 2006.

Information & Authors

Information

Published In

cover image Tissue Engineering Part A
Tissue Engineering Part A
Volume 26Issue Number 23-24December 2020
Pages: 1359 - 1368
PubMed: 32940144

History

Published online: 16 December 2020
Published in print: December 2020
Published ahead of print: 15 October 2020
Published ahead of production: 17 September 2020
Accepted: 14 September 2020
Received: 8 July 2020

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Authors

Affiliations

Anja Conev*
Department of Computer Science and Rice University, Houston, Texas, USA.
Eleni E. Litsa*
Department of Computer Science and Rice University, Houston, Texas, USA.
Marissa R. Perez
Department of Bioengineering, Rice University, Houston, Texas, USA.
NIH/NIBIB Center for Engineering Complex Tissues, USA.
Mani Diba
Department of Bioengineering, Rice University, Houston, Texas, USA.
NIH/NIBIB Center for Engineering Complex Tissues, USA.
Antonios G. Mikos
Department of Bioengineering, Rice University, Houston, Texas, USA.
NIH/NIBIB Center for Engineering Complex Tissues, USA.
Lydia E. Kavraki [email protected]
Department of Computer Science and Rice University, Houston, Texas, USA.

Notes

*
These authors contributed equally to this work.
Address correspondence to: Lydia E. Kavraki, PhD, Department of Computer Science, MS 132, Rice University, 6100 Main Street, Houston, TX 77005, USA [email protected]

Disclosure Statement

No competing financial interests exist.

Funding Information

We acknowledge support toward 3D printing of tissue engineering scaffolds from the National Institutes of Health (P41 EB023833) (AGM) and Rice University Funds (LEK). We also acknowledge support from a National Science Foundation Graduate Research Fellowship (MRP) and a Rubicon postdoctoral fellowship from the Netherlands Organization for Scientific Research (Project No. 019.182EN.004) (MD).

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