Transfer learning (TL) based additive manufacturing (AM) modeling is an emerging field to reuse the data from historical products and mitigate the data insufficiency in modeling new products. Although some trials have been conducted recently, the inherent challenges of applying TL in AM modeling are seldom discussed, e.g., which source domain to use, how much target data is needed, and whether to apply data preprocessing techniques. This paper aims to answer those questions through a case study defined based on an open-source dataset about metal AM products. In the case study, five TL methods are integrated with decision tree regression (DTR) and artificial neural network (ANN) to construct six TL-based models, whose performances are then compared with the baseline DTR and ANN in a proposed validation framework. The comparisons are used to quantify the performance of applied TL methods and are discussed from the perspective of similarity, training data size, and data preprocessing. Finally, the source AM domain with larger qualitative similarity and a certain range of target-to-source training data size ratio are recommended. Besides, the data preprocessing should be performed carefully to balance the modeling performance and the performance improvement due to TL.

翻译：迁移学习（TL）在增材制造（AM）建模中的应用是一个新兴领域，旨在通过复用历史产品的数据来缓解新产品质量建模中数据不足的问题。尽管近期已开展若干试验，但迁移学习在增材制造建模中应用所固有的挑战鲜有探讨，例如：应使用哪个源域、需要多少目标数据、以及是否应用数据预处理技术。本文通过一项基于金属增材制造产品开源数据集定义的案例研究来回答上述问题。在该案例研究中，将五种迁移学习方法与决策树回归（DTR）和人工神经网络（ANN）相结合，构建了六种基于迁移学习的模型，并在所提出的验证框架中将其性能与基准DTR和ANN进行对比。通过对比量化了所应用迁移学习方法的性能，并从相似性、训练数据量和数据预处理三个角度进行了讨论。最终，建议选择定性相似度较大的源增材制造域，并设定目标与源训练数据量比值的特定范围。此外，需谨慎执行数据预处理，以平衡建模性能与迁移学习带来的性能提升。