Human–Artificial Intelligence Teaming for Scientific Information Extraction From Data-Driven Additive Manufacturing Literature Using Large Language Models Available to Purchase

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. It requires substantial effort and time to extract scientific information from these works. AM domain experts have contributed over two dozen review articles 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 bidirectional encoder representations for transformers or generative pre-trained transformers on text sequences 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 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.