Application of Machine Learning Algorithms and Models in 3D Printing

Abstract

These days, three-dimensional printing is creating a huge hue in the manufacturing industry and also accumulating attention from various fields because of its capability to fabricate different parts having complex features; this advanced printing is known as Additive Manufacturing (AM). The AM processing parameter is highly effective on the microstructure that is printed, and the final product also depends upon the performance on these, controlling the AM processing parameters is quite tactful. There is no need for definite models to develop and mitigate the physical problems that are underlying as machine learning is successful for recognizing difficult patterns and regression analysis. Due to the presence of effective computational power, and sophisticated algorithm structure, the machine learning (ML) algorithm includes the neural network (NN) that is widely used to perform variable tasks on the large data set that are present at the moment. The neural network algorithm is attached to various parameters of the additive manufacturing chain, such as quality evaluation, in situ monitoring, and model design and the progress of these mechanisms is evaluated in this project. The application of Machine Learning in 3D printing of several aspects is discussed in the review article. There are also highlights of Machine Learning in 3D printing for potential uses and limitations.