Machine Learning for Next-generation Printed Technologies

Litty Varghese Thekkekara; Shamini P.  Baby; Jeffery Chan; Ivan  Cole

Machine Learning for Next-generation Printed Technologies

Litty Varghese Thekkekara （ Functional Material and Microsystems Research Group and the Micro Nano Research Facility, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia ）

Shamini P. Baby （ School of Computer Science, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia ）

Jeffery Chan （ School of Computer Science, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia ）

Ivan Cole （ School of Engineering, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia ）

https://doi.org/10.37155/2717-526X-0301-5

Abstract

Modern science advances towards the development of lightweight wearable and portable applications for the promotion of human-machine interfaces. Among them, the most beneficial ones include the technologies for healthcare, telecommunications, and energy resources. Recent developments in the additive manufacturing otherwise 3D printing sector are promising for largescale applications. It promotes cost-effective production of technologies like sensors, lab on chips, solar cells, and energy storage. However, these applications' efficiency is lower to technologies fabricated using other methods like chemical approaches due to the non-optimized parameters involved in the fabrication and characterization phases. Machine learning on the other hand expands its science and engineering capabilities. It has a broader opportunity to support 3D printing to develop the potentials and efficiency through effective prediction methods for printing methods and design aspects. In this review, we discuss the use of machine learning prediction algorithms for technologies using 3D printing.

Keywords

Additive manufacturing; 3D printing; Printed technologies; Artificial intelligence; Machine learning; Data analysis

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Publishing time:2021-06-30
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