Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163124
Title: Applications of machine learning in 3D printing
Authors: Goh, Guo Dong
Yeong, Wai Yee
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Goh, G. D. & Yeong, W. Y. (2022). Applications of machine learning in 3D printing. Materials Today: Proceedings. https://dx.doi.org/10.1016/j.matpr.2022.08.551
Journal: Materials Today: Proceedings
Abstract: The 4th Industrial Revolution integrates the digital revolution into the physical world, opens up new directions for research in several fields, including artificial intelligence and 3D printing. Data-driven models have been developed thanks to the advancement in computational power and machine learning algorithms, which enables machines to learn and execute a certain task through data without the need to formulate them explicitly. Machine learning have been used in 3D printing to enhance the product quality and productivity through in-situ monitoring, to optimize design and process parameters, and to speed up the prediction microstructure evolution through the development of physic-based data driven surrogate model. This article summarizes the work done by our group on the use of machine learning in 3D printing and provides an outlook on the 3D printing industry.
URI: https://hdl.handle.net/10356/163124
ISSN: 2214-7853
DOI: 10.1016/j.matpr.2022.08.551
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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