A Perspective on Using Machine Learning in 3D Bioprinting

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.

[1]  Marc Peter Deisenroth,et al.  Deep Reinforcement Learning: A Brief Survey , 2017, IEEE Signal Processing Magazine.

[2]  Rich Caruana,et al.  An empirical comparison of supervised learning algorithms , 2006, ICML.

[3]  Jianzhong Fu,et al.  Micro/nanofabrication of brittle hydrogels using 3D printed soft ultrafine fiber molds for damage-free demolding , 2019, Biofabrication.

[4]  Yung C. Shin,et al.  In-Process monitoring of porosity during laser additive manufacturing process , 2019, Additive Manufacturing.

[5]  A. Chiba,et al.  Simple method to construct process maps for additive manufacturing using a support vector machine , 2019, Additive Manufacturing.

[6]  Xun Xu,et al.  Support Structures for Additive Manufacturing: A Review , 2018, Journal of Manufacturing and Materials Processing.

[7]  Richard K. Leach,et al.  Rapid tracking of extrinsic projector parameters in fringe projection using machine learning , 2019 .

[8]  Robert X. Gao,et al.  Machine learning-based image processing for on-line defect recognition in additive manufacturing , 2019, CIRP Annals.

[9]  Zhixiong Li,et al.  Prediction of surface roughness in extrusion-based additive manufacturing with machine learning , 2019, Robotics and Computer-Integrated Manufacturing.

[10]  Jianzhong Fu,et al.  Electro-Assisted Bioprinting of Low-Concentration GelMA Microdroplets. , 2019, Small.

[11]  Edward W. Frees,et al.  Predictive Modeling Applications in Actuarial Science , 2014 .

[12]  JiangJingchao,et al.  Support Optimization for Flat Features via Path Planning in Additive Manufacturing , 2019, 3D Printing and Additive Manufacturing.

[13]  Jingchao Jiang,et al.  Optimization of process planning for reducing material waste in extrusion based additive manufacturing , 2019 .

[14]  Jianzhong Fu,et al.  Vessel-on-a-chip with Hydrogel-based Microfluidics. , 2018, Small.

[15]  Linkan Bian,et al.  Quantifying Geometric Accuracy With Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts , 2018 .

[16]  Qiang Huang,et al.  Machine learning in tolerancing for additive manufacturing , 2018 .

[17]  Ray Y. Zhong,et al.  Investigation of printable threshold overhang angle in extrusion-based additive manufacturing for reducing support waste , 2018, Int. J. Comput. Integr. Manuf..

[18]  Oleksandr Semeniuta,et al.  Optimization of Process Parameters for Powder Bed Fusion Additive Manufacturing by Combination of Machine Learning and Finite Element Method: A Conceptual Framework , 2018 .

[19]  Peter Borgesen,et al.  Classifying the Dimensional Variation in Additive Manufactured Parts From Laser-Scanned Three-Dimensional Point Cloud Data Using Machine Learning Approaches , 2017 .

[20]  Jingchao Jiang,et al.  Effect of support on printed properties in fused deposition modelling processes , 2019, Virtual and Physical Prototyping.

[21]  Chee Kai Chua,et al.  A Perspective on 4D Bioprinting , 2016 .

[22]  Yang Yang,et al.  Machine learning for continuous liquid interface production: Printing speed modelling , 2019, Journal of Manufacturing Systems.

[23]  Wei Long Ng,et al.  Print Me An Organ! Why We Are Not There Yet , 2019, Progress in Polymer Science.

[24]  Xun Xu,et al.  A benchmarking part for evaluating and comparing support structures of additive manufacturing , 2018 .

[25]  Markus J. Buehler,et al.  Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment , 2018 .

[26]  Ping Guo,et al.  A novel strategy to fabricate thin 316L stainless steel rods by continuous directed energy deposition in Z direction , 2019, Additive Manufacturing.

[27]  Linkan Bian,et al.  Deep Learning for Distortion Prediction in Laser-Based Additive Manufacturing using Big Data , 2019, Manufacturing Letters.

[28]  Charlie C. L. Wang,et al.  Current and future trends in topology optimization for additive manufacturing , 2018 .

[29]  MenonAditya,et al.  Optimization of Silicone 3D Printing with Hierarchical Machine Learning , 2019, 3D Printing and Additive Manufacturing.

[30]  Malcolm Xing,et al.  3D bioprinting for biomedical devices and tissue engineering: A review of recent trends and advances , 2018, Bioactive materials.

[31]  Jack Beuth,et al.  Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process , 2019, Additive Manufacturing.

[32]  Terrence J. Sejnowski,et al.  Unsupervised Learning , 2018, Encyclopedia of GIS.

[33]  Xiao Li,et al.  Analysis and prediction of printable bridge length in fused deposition modelling based on back propagation neural network , 2019, Virtual and Physical Prototyping.

[34]  Martin L. Dunn,et al.  Machine-learning based design of active composite structures for 4D printing , 2019, Smart Materials and Structures.

[35]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[36]  Xun Xu,et al.  Optimisation of multi-part production in additive manufacturing for reducing support waste , 2019, Virtual and Physical Prototyping.