Review of transfer learning in modeling additive manufacturing processes

Modeling plays an important role in the additive manufacturing (AM) process and quality control. In practice, however, only limited data are available for each product due to the relatively high AM cost, which brings challenges in building either a high-quality physics-based or data-based model. Transfer learning (TL) is a new and promising group of approaches where the model of one product (source) may be reused for another product (target) with limited new target data. This paper focuses on reviewing applications of TL in AM modeling to help advance research in this area. First, notations, definitions, and categories of TL methods are introduced along with their application scenarios. Then current applications of TL in AM modeling are summarized along with their limitations. Based on reviewed applications, recommendations are given on how to apply TL for a certain AM problem, from the perspectives of source domain determination, TL method selection, target data generation, and data preprocessing. Finally, future research directions about TL in AM modeling are discussed in the hope to explore more potential of TL in improving the AM model quality with limited data.