Machine Learning in 3D Printing and Bioprinting, a Collection of Recent Publications

In this issue, we included three articles focusing on the current state of machine learning in 3D printing and bioprinting, tissue engineering. Machine learning and artificial intelligence could become the foundation of the next generation of 3D printing and tissue engineering. “From Academia” features recent, relevant, close to commercialization academic publications in the space of healthcare 3D printing, 3D bioprinting, and related emerging technologies.

Email: Rance Tino ([email protected]) if you want to pen an Expert Corner blog for us or want to share relevant academic publications with us.

A review on machine learning in 3D printing: applications, potential, and challenges

– Authored by G. D. Goh, S. L. Sing & W. Y. Yeong. Artificial Intelligence Review. 16 July 2020

Machine Learning in 3D printing
Summary of AI in 3D printing.Copyright. Artificial Intelligence Review
Machine Learning in 3D printing
Workflow of cyberattack detection in AM using ML (Faruque 2016). Copyright. Artificial Intelligence Review


Additive manufacturing (AM) or 3D printing is growing rapidly in the manufacturing industry and has gained a lot of attention from various fields owing to its ability to fabricate parts with complex features. The reliability of the 3D printed parts has been the focus of the researchers to realize AM as an end-part production tool.

Machine learning (ML) has been applied in various aspects of AM to improve the whole design and manufacturing workflow especially in the era of industry 4.0. In this review article, various types of ML techniques are first introduced.

It is then followed by the discussion on their use in various aspects of AM such as design for 3D printing, material tuning, process optimization, in situ monitoring, cloud service, and cybersecurity. Potential applications in biomedical, tissue engineering, and building and construction will be highlighted.

The challenges faced by ML in AM such as computational cost, standards for qualification, and data acquisition techniques will also be discussed. In the authors’ perspective, in situ monitoring of AM processes will significantly benefit from the object detection ability of ML. As a large data set is crucial for ML, data sharing of AM would enable faster adoption of ML in AM. Standards for the shared data are needed to facilitate the easy sharing of data. The use of ML in AM will become more mature and widely adopted as better data acquisition techniques and more powerful computer chips for ML are developed.

Deep learning for fabrication and maturation of 3D bioprinted tissues and organs

– Authored by Wei Long Ng, Alvin Chan, Yew Soon Ong & Chee Kai Chua. Virtual and Physical Prototyping, 16 May 2020

Machine Learning in 3D printing
A) An example of supervised learning where the model is trained to predict cell stage. Training data include input data of cell images (x) and corresponding labels of expert-annotated cell stages (y). (B) Unsupervised learning example of super-resolution where the model learns from high-resolution images. The low-resolution input data can be automatically generated without laborious human annotation. (C) Reinforcement learning task of parameter optimisation where desired outcome such as resolution, fabrication speed and cell viability can be used as a reward for the trained model. Copyright. Virtual and Physical Prototyping

Machine Learning in 3D printing
(A) Systematic drawing of an organ-on-a-chip system that incorporates microfluidic concentration gradient generator designs to facilitates the facile and rapid generation of co-culture medium formulations of varying concentrations. Adapted and reproduced with permission (Paik, Sim, and Jeon 2017). (B) The use of external stimuli (mechanical, electrical etc) in tissue conditioning process to enhance the maturation and functionality of 3D bioprinted tissue constructs. Adapted and reproduced with permission (Kim et al. 2018). Copyright. Virtual and Physical Prototyping


Bioprinting is a relatively new and promising tissue engineering approach to solve the problem of donor shortage for organ transplantation. It is a highly-advanced biofabrication system that enables the printing of materials in the form of biomaterials, living cells, and growth factors in a layer-by-layer manner to manufacture 3D tissue-engineered constructs.

The current workflow involves a myriad of manufacturing complexities, from medical image processing to optimization of printing parameters and refinements during post-printing tissue maturation.

Deep learning is a powerful machine learning technique that has fuelled remarkable progress in image and language applications over the past decade. In this perspective paper, we highlight the integration of deep learning into 3D bioprinting technology and the implementation of practical guidelines. We address potential adoptions of deep learning into various 3D bioprinting processes such as image-processing and segmentation, optimization and in-situ correction of printing parameters, and lastly refinement of the tissue maturation process.

Finally, we discuss the implications that deep learning has on the adoption and regulation of 3D bioprinting. The synergistic interactions among the field of biology, material, and deep learning-enabled computational design will eventually facilitate the fabrication of biomimetic patient-specific tissues/organs, making 3D bioprinting of tissues/organs an impending reality. 

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

– Authored by Anja Conev, Eleni E. Litsa, Marissa R. Perez, Mani Diba, Antonios G. Mikos, and Lydia E. Kavraki. Tissue Engineering Part A. 15 October 2020

Machine Learning in 3D printing
Representative images of low (A) and high quality (C) prints based on machine precision (of machine precision of 9.80% and 2.65%, and material accuracy of 3.20% and 32.58%, respectively), and low (B) and high quality (D) prints based on material accuracy (of machine precision of 1.75% and 4.90%, and material accuracy of 70.56% and 3.86%, respectively). The printing configurations (layer, material composition, printing pressure, printing speed, programmed spacing) for the images are as follows: A) layer 6, 85 wt% PPF, 2.5 bar, 7.5 mm/s, 1.2 mm, B) layer 3, 85 wt% PPF, 2.5 bar, 20 mm/s, 1.2 mm, C) layer 3, 85wt% PPF, 2 bar, 10 mm/s, 1.2 mm, and D) layer 4, 85 wt% PPF, 2 bar, 5 mm/s, 1.2 mm. Scale bar = 1 mm. Copyright. Tissue Engineering Part A
Machine Learning and Artificial Intelligence in 3D Printing and Bioprinting
Ranking of the features (printing parameters), as obtained from the RFr model, based on their importance for affecting printing quality. Copyright. Tissue Engineering Part A


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 the 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.

From Academia: 3D Printing for Neurosurgery Training, Vat Photopolymerization, soft robotic microsystem

From Academia: 3D Printing Organoid, Bioelectronic Implant, Tensegrity Structures

From Academia: Open-Source 3D printed Medical Devices and New Sensor for COVID

From Academia: Tweaking Bioinks Palette, One-Drop 3D Printing

From Academia: Nanoclay Bioink, Machine Learning, Hydrogel Design Strategies for 3D Bioprinting

From Academia: 3D Printed PPE Safety, A Better hydrogel, Cadaver Replacement

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