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Research Article · Open access · Published 14 July 2018

A Survey on Recent Applications of Machine Learning with Big Data in Additive Manufacturing Industry

Micheal Omotayo Alabi1, Ken Nixon1 & Ionel Botef1

American Journal of Engineering and Applied Sciences, Volume 11, Issue 3, pp. 1114–1124 · doi.org/10.3844/ajeassp.2018.1114.1124

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Abstract

Additive Manufacturing (AM) which is also known as 3D printing technology; is recognized as a new paradigm for manufacturing industry. Additive manufacturing is rapidly expanding across different sectors such as healthcare, electronics, automotive, science and engineering, education, dental, etc. Machine Learning and Big Data are both emerging technologies which are becoming popular and gaining more attention from the industries and academic. Machine Learning is a growing field of Artificial Intelligence (AI) that allows systems to learn from data, identify patterns and make decisions with very little human involvement. On the other hand, Big Data is referred to as datasets whose size is more than the capacity of what a conventional database software tools can capture, store, manage and analyze. Lately, Machine Learning techniques and Big Data Analytics are being applied to various applications of additive manufacturing to monitor building process and enhance decisions making using data generated through different sensors or cameras. This paper explores recent applications of Machine Learning with Big Data in the field of additive manufacturing, for instance, application of machine learning in detecting defect or anomaly during build process in additive manufacturing/3D printing machine.

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Alabi, M. O., Nixon, K. & Botef, I. (2018). A Survey on Recent Applications of Machine Learning with Big Data in Additive Manufacturing Industry. American Journal of Engineering and Applied Sciences, 11(3), 1114–1124. https://doi.org/10.3844/ajeassp.2018.1114.1124

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A Survey on Recent Applications of Machine Learning with Big Data in Additive Manufacturing IndustryAmerican Journal of Engineering and Applied Sciences · Volume 11, Issue 3 · pp. 1114–1124 · CC BY 3.0Download