  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. It
requires substantial effort and time to extract scientific information from
these works. AM domain experts have contributed over two dozen review papers to
summarize these works. However, information specific to AM and AI contexts
still requires manual effort to extract. The recent success of foundation
models such as BERT (Bidirectional Encoder Representations for Transformers) or
GPT (Generative Pre-trained Transformers) on textual data has opened the
possibility of expediting scientific information extraction. We propose a
framework that enables collaboration between AM and AI experts to continuously
extract scientific information from data-driven AM literature. A demonstration
tool is implemented based on the proposed framework and a case study is
conducted to extract information relevant to the datasets, modeling, sensing,
and AM system categories. We show the ability of LLMs (Large Language Models)
to expedite the extraction of relevant information from data-driven AM
literature. In the future, the framework can be used to extract information
from the broader design and manufacturing literature in the engineering
discipline.
