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.
