Transferability analysis of data-driven additive manufacturing
  knowledge: a case study between powder bed fusion and directed energy
  deposition
Mutahar Safdar Jiarui Xie Hyunwoong Ko Yan Lu Guy Lamouche Yaoyao Fiona Zhao
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.