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

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...

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Main Authors: Goh, Guo Dong, Sing, Swee Leong, Yeong, Wai Yee
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/143418
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1434182023-03-04T17:18:53Z A review on machine learning in 3D printing : applications, potential, and challenges Goh, Guo Dong Sing, Swee Leong Yeong, Wai Yee School of Mechanical and Aerospace Engineering Singapore Centre for 3D Printing Engineering::Computer science and engineering Engineering::Manufacturing Additive Manufacturing Machine Learning 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 the 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 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. National Research Foundation (NRF) Accepted version This research is supported by the National Research Foundation, Prime Minister’s Ofce, Singapore under its Medium-Sized Centre funding scheme. 2020-08-31T06:22:33Z 2020-08-31T06:22:33Z 2020 Journal Article Goh, G. D., Sing, S. L., & Yeong, W. Y. (2020). A review on machine learning in 3D printing : applications, potential, and challenges. Artificial Intelligence Review, 1-32. doi:10.1007/s10462-020-09876-9 1573-7462UR https://hdl.handle.net/10356/143418 10.1007/s10462-020-09876-9 2-s2.0-85087973462 1 32 en Artificial Intelligence Review © 2020 Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in Artificial Intelligence Review. The final authenticated version is available online at https://doi.org/10.1007/s10462-020-09876-9 application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Engineering::Manufacturing
Additive Manufacturing
Machine Learning
spellingShingle Engineering::Computer science and engineering
Engineering::Manufacturing
Additive Manufacturing
Machine Learning
Goh, Guo Dong
Sing, Swee Leong
Yeong, Wai Yee
A review on machine learning in 3D printing : applications, potential, and challenges
description 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 the 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 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.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Goh, Guo Dong
Sing, Swee Leong
Yeong, Wai Yee
format Article
author Goh, Guo Dong
Sing, Swee Leong
Yeong, Wai Yee
author_sort Goh, Guo Dong
title A review on machine learning in 3D printing : applications, potential, and challenges
title_short A review on machine learning in 3D printing : applications, potential, and challenges
title_full A review on machine learning in 3D printing : applications, potential, and challenges
title_fullStr A review on machine learning in 3D printing : applications, potential, and challenges
title_full_unstemmed A review on machine learning in 3D printing : applications, potential, and challenges
title_sort review on machine learning in 3d printing : applications, potential, and challenges
publishDate 2020
url https://hdl.handle.net/10356/143418
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