Data-driven research in Additive Manufacturing (AM) has gained significant success in recent years. This has led to a plethora of scientific literature to emerge. The knowledge in these works consists of AM and Artificial Intelligence (AI) contexts that have not been mined and formalized in an integrated way. Moreover, no tools or guidelines exist to support data-driven knowledge transfer from one context to another. As a result, data-driven solutions using specific AI techniques are being developed and validated only for specific AM process technologies. There is a potential to exploit the inherent similarities across various AM technologies and adapt the existing solutions from one process or problem to another using AI, such as Transfer Learning. We propose a three-step knowledge transferability analysis framework in AM to support data-driven AM knowledge transfer. As a prerequisite to transferability analysis, AM knowledge is featurized into identified knowledge components. The framework consists of pre-transfer, transfer, and post-transfer steps to accomplish knowledge transfer. A case study is conducted between flagship metal AM processes. Laser Powder Bed Fusion (LPBF) is the source of knowledge motivated by its relative matureness in applying AI over Directed Energy Deposition (DED), which drives the need for knowledge transfer as the less explored target process. We show successful transfer at different levels of the data-driven solution, including data representation, model architecture, and model parameters. The pipeline of AM knowledge transfer can be automated in the future to allow efficient cross-context or cross-process knowledge exchange.

翻译：数据驱动的增材制造研究近年来取得了显著进展，催生了大量相关学术文献。这些文献中的知识包含增材制造与人工智能的上下文信息，但尚未以集成化方式进行挖掘与形式化。此外，目前缺乏支持数据驱动知识在不同情境间迁移的工具或指南。因此，基于特定人工智能技术的数据驱动解决方案仅针对特定增材制造工艺技术进行开发与验证。实际上，可利用不同增材制造技术间的固有相似性，通过迁移学习等人工程能方法将现有解决方案从一种工艺或问题适配至另一种。我们提出面向增材制造的三阶段知识可迁移性分析框架，以支持数据驱动的增材制造知识迁移。作为可迁移性分析的前提，将增材制造知识特征化为若干已识别的知识组件。该框架包含迁移前、迁移中、迁移后三个阶段以完成知识迁移。以旗舰级金属增材制造工艺开展案例研究：激光粉末床熔融（LPBF）作为知识源，因其在应用人工智能方面相较于定向能量沉积（DED）更为成熟，而后者作为待探索目标工艺驱动了知识迁移需求。我们在数据表示、模型架构及模型参数等不同数据驱动解决方案层级上实现了成功迁移。未来可实现增材制造知识迁移管道的自动化，以支持高效跨情境或跨工艺知识交换。