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Machine Learning Applications for 3D-Printed Polymers and Their Composites

Machine Learning Applications for 3D-Printed Polymers and Their Composites

Mamta B. Savadatti, Kiran Kumar N., Jaya Christiyan K. G., Amithkumar Gajakosh, Mukesh Thakur, R. Suresh Kumar, Richard Lincoln Paulraj, Madhusudhana H. K.
Copyright: © 2023 |Pages: 22
ISBN13: 9781668460092|ISBN10: 1668460092|EISBN13: 9781668460115
DOI: 10.4018/978-1-6684-6009-2.ch014
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MLA

Savadatti, Mamta B., et al. "Machine Learning Applications for 3D-Printed Polymers and Their Composites." Development, Properties, and Industrial Applications of 3D Printed Polymer Composites, edited by R. Keshavamurthy, et al., IGI Global, 2023, pp. 239-260. https://doi.org/10.4018/978-1-6684-6009-2.ch014

APA

Savadatti, M. B., Kiran Kumar N., Jaya Christiyan K. G., Gajakosh, A., Thakur, M., Kumar, R. S., Paulraj, R. L., & Madhusudhana H. K. (2023). Machine Learning Applications for 3D-Printed Polymers and Their Composites. In R. Keshavamurthy, V. Tambrallimath, & J. Davim (Eds.), Development, Properties, and Industrial Applications of 3D Printed Polymer Composites (pp. 239-260). IGI Global. https://doi.org/10.4018/978-1-6684-6009-2.ch014

Chicago

Savadatti, Mamta B., et al. "Machine Learning Applications for 3D-Printed Polymers and Their Composites." In Development, Properties, and Industrial Applications of 3D Printed Polymer Composites, edited by R. Keshavamurthy, Vijay Tambrallimath, and J. Paulo Davim, 239-260. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-6009-2.ch014

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Abstract

Although the number of applications for 3D printing has substantially risen over the past several years, it is required to calibrate the AM processing settings. Various methods of AL are being applied in today's world in order to improve the parameters of 3D printing and to forecast the quality of components that have been 3D printed. An application of ML in the prediction of the properties and performance of 3D-printed components has been demonstrated in the current work. This research begins with an introduction to machine learning and continues with a summary of its uses in the 3D printing process. The majority of this chapter is dedicated to discussing the applications of ML in the forecasting of essential properties of 3D-printed components. In order to accomplish this objective, prior research studies that studied the application of ML in the characterisation of polymeric and polymer composites have been reviewed and addressed.

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