Understanding of the Modeling Method in Additive Manufacturing

Abstract

Abstract With the development of additive manufacturing, how to improve the efficiency and accuracy of manufacturing and prediction has attracted more and more attention. This paper focuses on three basic modeling methods, which are summarized according to the issue: empirical method, analytical method and numerical method. These methods are used differently based on practical circumstances. Besides, due to the improvement of computer computing power, machine learning and digital twin have also been applied to the study of additive manufacturing. Machine learning has a good performance in the prediction and optimization of process parameters, but the characteristic of machine learning that requires a lot of data leads to the increase of experimental cost. Digital twin does a good job in monitoring the condition of equipment. In addition, it can replace expensive and time-consuming physical experiments with inexpensive and efficient digital experiments, which can provide data for analysis. However, because of insufficient research, its application is still limited.