AccScience Publishing / IJB / Volume 6 / Issue 1 / DOI: 10.18063/ijb.v6i1.253
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PERSPECTIVE ARTICLE

A Perspective on Using Machine Learning in 3D Bioprinting

Chunling Yu1 Jingchao Jiang2*
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1 Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
2 Department of Mechanical Engineering, University of Auckland, Auckland 1010, New Zealand
Published: 24 January 2020
© 2020 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

Recently, three-dimensional (3D) printing technologies have been widely applied in industry and our daily lives. The term 3D bioprinting has been coined to describe 3D printing at the biomedical level. Machine learning is currently becoming increasingly active and has been used to improve 3D printing processes, such as process optimization, dimensional accuracy analysis, manufacturing defect detection, and material property prediction. However, few studies have been found to use machine learning in 3D bioprinting processes. In this paper, related machine learning methods used in 3D printing are briefly reviewed and a perspective on how machine learning can also benefit 3D bioprinting is discussed. We believe that machine learning can significantly affect the future development of 3D bioprinting and hope this paper can inspire some ideas on how machine learning can be used to improve 3D bioprinting.

Keywords
3D printing
Bioprinting
Machine learning
References

1. Ng WL, Yeong WY, 2019, The Future of Skin Toxicology Testing 3D Bioprinting Meets Microfluidics. Int J Bioprinting, 5:237. DOI: 10.18063/ijb.v5i2.1.237.

2. Jiang J, Weng F, Gao S, et al., 2019, A Support Interface Method for Easy Part Removal in Direct Metal Deposition. Manuf Lett, 20:30–3. DOI: 10.1016/j.mfglet.2019.04.002.

3. Liu J, Gaynor AT, Chen S, et al., 2018, Current and Future Trends in Topology Optimization for Additive Manufacturing. Struct Multidiscipl Optim, 57:2457–83. DOI: 10.1007/s00158-018-1994-3.

4. Jiang J, Xu X, Stringer J, 2018, Support Structures for Additive Manufacturing: A Review. J Manuf Mater Process, 2:64. DOI: 10.3390/jmmp2040064.

5. Jiang J, Xu X, Stringer J, 2019, Optimisation of Multipart Production in Additive Manufacturing for Reducing Support Waste. Virtual Phys Prototyp, 14:219–28. DOI: 10.1080/17452759.2019.1585555.

6. Weng F, Gao S, Jiang J, et al., 2019, A Novel Strategy to Fabricate Thin 316L Stainless Steel Rods by Continuous Direct Metal Deposition in Z Direction. Addit Manuf, 27:474–81. DOI:10.1016/j.addma.2019.03.024.

7. Jiang J, Xu X, Stringer J, 2018, A New Support Strategy for Reducing Waste in Additive Manufacturing. In: The 48th International Conference on Computers and Industrial Engineering (CIE 48). Curran Associates, Inc., Auckland. pp. 1–7.

8. Jiang J, Stringer J, Xu X, et al., 2018, A Benchmarking Part for Evaluating and Comparing Support Structures of Additive Manufacturing. In: 3rd International Conference on Progress in Additive Manufacturing (Pro-AM 2018). Singapore. pp. 196–202.

9. Lv S, Nie J, Gao Q, et al., 2019, Micro/Nanofabrication of Brittle Hydrogels Using 3D Printed Soft Ultrafine Fiber Molds for Damage-free Demolding. Biofabrication. DOI:10.1088/1758-5090/ab57d8.

10. Nie J, Gao Q, Wang Y, et al., 2018, Vessel-on-a-chip with Hydrogel-Based Microfluidics. Small, 14:1802368. DOI: 10.1002/smll.201802368.

11. An J, Chua CK, Mironov V, 2016, A Perspective on 4D Bioprinting. Int J Bioprinting, 2:3–5. DOI: 10.18063/IJB.2016.01.003.

12. Jiang J, Xu X, Stringer J, 2019, Optimization of Process Planning for Reducing Material Waste in Extrusion Based Additive Manufacturing. Robot Comput Integr Manuf, 59:317–25. DOI: 10.1016/j.rcim.2019.05.007.

13. Jiang J, Stringer J, Xu X, et al., 2018, Investigation of Printable Threshold Overhang Angle in Extrusion based Additive Manufacturing for Reducing Support Waste. Int J Comput Integr Manuf, 31:961–9. DOI: 10.1080/0951192X.2018.1466398.

14. Jiang J, Lou J, Hu G, 2019, Effect of Support on Printed Properties in Fused Deposition Modelling Processes. Virtual Phys Prototyp, 14:308–15. DOI: 10.1080/17452759.2019.1568835.

15. Ng WL, Chua CK, Shen YF, 2019, Print Me An Organ! Why We Are Not There Yet. Prog Polym Sci, 97:101145. DOI: 10.1016/j.progpolymsci.2019.101145.

16. Derakhshanfar S, Mbeleck R, Xu K, et al., 2018, 3D Bioprinting for Biomedical Devices and Tissue Engineering: A Review of Recent Trends and Advances. Bioact Mater, 3:144–56. DOI: 10.1016/j.bioactmat.2017.11.008.

17. Aoyagi K, Wang H, Sudo H, et al., 2019, Simple Method to Construct Process Maps for Additive Manufacturing Using a Support Vector Machine. Addit Manuf, 27:353–62. DOI:10.1016/j.addma.2019.03.013.

18. Menon A, Póczos B, Feinberg AW, et al., 2019, Optimization of Silicone 3D Printing with Hierarchical Machine Learning. 3D Print Addit Manuf, 6:181–9. DOI: 10.1089/3dp.2018.0088.

19. He H, Yang Y, Pan Y, 2019, Machine Learning for Continuous Liquid Interface Production: Printing Speed Modelling. J Manuf Syst, 50:236–46. DOI: 10.1016/j.jmsy.2019.01.004.

20. Stavroulakis P, Chen S, Delorme C, et al., 2019, Rapid Tracking of Extrinsic Projector Parameters in Fringe Projection Using Machine Learning. Opt Lasers Eng, 114:7–14. DOI: 10.1016/j.optlaseng.2018.08.018.

21. Baturynska I, Semeniuta O, Martinsen K, 2018, Optimization of Process Parameters for Powder Bed Fusion Additive Manufacturing by Combination of Machine Learning and Finite Element Method: A Conceptual Framework. In: Procedia CIRP. Elsevier B.V., Heidelberg. pp. 227–32. DOI:10.1016/j.procir.2017.12.204.

22. Francis J, Bian L, 2019, Deep Learning for Distortion Prediction in Laser-Based Additive Manufacturing Using Big Data. Manuf Lett, 20:10–4. DOI: 10.1016/j. mfglet.2019.02.001.

23. Khanzadeh M, Rao P, Jafari-Marandi R, et al., 2018, Quantifying Geometric Accuracy with Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts. J Manuf Sci Eng, 140:301011. DOI: 10.1115/1.4038598.

24. Zhu Z, Anwer N, Huang Q, et al., 2018, Machine Learning in Tolerancing for Additive Manufacturing. CIRP Ann, 67:157–60. DOI: 10.1016/j.cirp.2018.04.119.

25. Tootooni MS, Dsouza A, Donovan R, et al., 2017, Classifying the Dimensional Variation in Additive Manufactured Parts from Laser-Scanned Three-Dimensional Point Cloud Data Using Machine Learning Approaches. J Manuf Sci Eng, 139:091005. DOI: 10.1115/1.4036641.

26. 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. Addit Manuf, 25:151–65. DOI: 10.1016/j.addma.2018.11.010.

27. Caggiano A, Zhang J, Alfieri V, et al., 2019, Machine Learning-based Image Processing for On-Line Defect Recognition in Additive Manufacturing. CIRP Ann, 68:451–4. DOI: 10.1016/j.cirp.2019.03.021.

28. Zhang B, Liu S, Shin YC, 2019, In-Process Monitoring of Porosity During Laser Additive Manufacturing Process. Addit Manuf, 28:497–505. DOI: 10.1016/j.addma.2019.05.030.

29. Gu GX, Chen CT, Richmond DJ, et al, 2018, Bioinspired Hierarchical Composite Design Using Machine Learning: Simulation, Additive Manufacturing, and Experiment. Mater Horizons, 5:939–45. DOI: 10.1039/c8mh00653a.

30. Hamel CM, Roach DJ, Long KN, et al., 2019, Machine-Learning Based Design of Active Composite Structures for 4D Printing. Smart Mater Struct, 28:065005. DOI: 10.1088/1361-665X/ab1439.

31. Li Z, Zhang Z, Shi J, et al., 2019, Prediction of Surface Roughness in Extrusion-Based Additive Manufacturing with Machine Learning. Robot Comput Integr Manuf, 57:488–95.DOI: 10.1016/j.rcim.2019.01.004.

32. Jiang J, Hu G, Li X, et al., 2019, Analysis and Prediction of Printable Bridge Length in Fused Deposition Modelling Based on Back Propagation Neural Network. Virtual Phys Prototyp,14:253–66. DOI: 10.1080/17452759.2019.1576010.

33. Caruana R, Niculescu-Mizil A, 2006, An Empirical Comparison of Supervised Learning Algorithms. In: ACM International Conference Proceeding Series. ACM, Pittsburgh. pp. 161–8. DOI: 10.1145/1143844.1143865.

34. Francis L, 2014, Unsupervised Learning. In: Predictive Modeling Applications in Actuarial Science. Predictive Modeling Techniques. Vol. 1. Cambridge University Press, Philadelphia, PA.

35. Arulkumaran K, Deisenroth MP, Brundage M, et al., 2017, Deep Reinforcement Learning: A Brief Survey. IEEE Signal Process Mag, 34:26–38. DOI: 10.1109/msp.2017.2743240.

36. Jordan MI, Mitchell TM, 2015, Machine Learning: Trends, Perspectives, and Prospects. Science, 349:255–60.

37. Xie M, Gao Q, Zhao H, et al., 2019, Electro-Assisted Bioprinting of Low-Concentration GelMA Microdroplets. Small, 15:1804216. DOI: 10.1002/smll.201804216.

38. Jiang J, Stringer J, Xu X, 2019, Support Optimization for Flat Features via Path Planning in Additive Manufacturing. 3D Print Addit Manuf, 6:171–9. DOI: 10.1089/3dp.2017.0124.

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International Journal of Bioprinting, Electronic ISSN: 2424-8002 Print ISSN: 2424-7723, Published by AccScience Publishing