Review of Transfer Learning in Additive Manufacturing Modeling

Abstract

Abstract The process-structure-property modeling of additive manufacturing (AM) products plays an important role in process and quality control. In practice however, only limited data are available for each product due to its expensive material and time-consuming fabricating process, which becomes an obstacle to achieve high quality models. Transfer learning (TL) is a new and promising approach that the model of one product (source) may be reused for another product (target) with limited new data on the target. This paper focuses on reviewing applications of TL in AM modeling in order to help further research in this area. To clarify the specific topic, the problem definition is presented, as well as the differences between TL, multi-fidelity modeling, and multi-task learning. Then current applications of TL in AM modeling are summarized according to different TL approaches. To better understand the performances of different TL approaches, several representative TL-assisted AM modeling methods are reproduced and tested on an open-source dataset. Based on the test results, their effectiveness and limitations are discussed in detail. Finally, future research directions about TL in AM modeling are discussed in hope to explore more potential of TL in boosting the AM model performance.