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Machine Learning Applications for 3D-Printed Polymers and Their Composites
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Author(s): Mamta B. Savadatti (New Horizon College of Engineering, India), Kiran Kumar N. (Vemana Institute of Technology, India), Jaya Christiyan K. G. (M.S. Ramaiah Institute of Technology, India), Amithkumar Gajakosh (Dayananda Sagar College of Engineering, India), Mukesh Thakur (NMDC DAV Polytechnic, India), R. Suresh Kumar (BMS College of Engineering, India), Richard Lincoln Paulraj (New Horizon College of Engineering, India)and Madhusudhana H. K. (KLE Technological University, India)
Copyright: 2023
Pages: 22
Source title:
Development, Properties, and Industrial Applications of 3D Printed Polymer Composites
Source Author(s)/Editor(s): R. Keshavamurthy (Dayananda Sagar College of Engineering, India), Vijay Tambrallimath (Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India)and J. Paulo Davim (University of Aveiro, Portugal)
DOI: 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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