A Consolidated Approach towards Application of Machine Learning Principles in Additive Manufacturing

Abstract

In recent years, additive manufacturing (AM) has garnered significant attention all over the world due to the exemplary benefits attained during design to achieving superior part quality. Researchers have also started utilizing machine learning (ML) tools to aid the AM process. Emphasis has been laid on the availability of ample datasets and the ease of their acquisition. The need for establishment of feature libraries has been highlighted. Different ML techniques and associated models such as Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Trees (DT), Deep Convolution Network (DNN), and Convolutional Neural Network (CNN) are being used by researchers for optimization of parameters, defect detection, creation of online monitoring systems as well as predicting the powder spreading mechanism for AM. In fact, most ML tools are utilized either for classification or regression purposes. This paper focuses on the availability of the resources required to employ ML in AM, the applications of ML in AM, present limitations, and potential opportunities for extended use in future.